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2088 lines
92 KiB
Python
2088 lines
92 KiB
Python
import math
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import matplotlib.pyplot as plt
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import numpy as np
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from shapely import geometry
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import cv2
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import imutils
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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from .is_nan import isNaN
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def crop_image_inside_box(box, img_org_copy):
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image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
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return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
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def otsu_copy(img):
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img_r = np.zeros(img.shape)
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img1 = img[:, :, 0]
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img2 = img[:, :, 1]
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img3 = img[:, :, 2]
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# print(img.min())
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# print(img[:,:,0].min())
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# blur = cv2.GaussianBlur(img,(5,5))
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# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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return img_r
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def otsu_copy_binary(img):
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img_r = np.zeros((img.shape[0], img.shape[1], 3))
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img1 = img[:, :, 0]
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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img_r = img_r / float(np.max(img_r)) * 255
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return img_r
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def find_features_of_lines(contours_main):
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areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
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cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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slope_lines = []
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for kk in range(len(contours_main)):
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[vx, vy, x, y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2, 0, 0.01, 0.01)
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slope_lines.append(((vy / vx) / np.pi * 180)[0])
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slope_lines_org = slope_lines
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slope_lines = np.array(slope_lines)
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slope_lines[(slope_lines < 10) & (slope_lines > -10)] = 0
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slope_lines[(slope_lines < -200) | (slope_lines > 200)] = 1
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slope_lines[(slope_lines != 0) & (slope_lines != 1)] = 2
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dis_x = np.abs(x_max_main - x_min_main)
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return slope_lines, dis_x, x_min_main, x_max_main, np.array(cy_main), np.array(slope_lines_org), y_min_main, y_max_main, np.array(cx_main)
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def boosting_headers_by_longshot_region_segmentation(textregion_pre_p, textregion_pre_np, img_only_text):
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textregion_pre_p_org = np.copy(textregion_pre_p)
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# 4 is drop capitals
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headers_in_longshot = (textregion_pre_np[:, :, 0] == 2) * 1
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# headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1
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textregion_pre_p[:, :, 0][(headers_in_longshot[:, :] == 1) & (textregion_pre_p[:, :, 0] != 4)] = 2
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textregion_pre_p[:, :, 0][textregion_pre_p[:, :, 0] == 1] = 0
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# textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted
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textregion_pre_p[:, :, 0][(img_only_text[:, :] == 1) & (textregion_pre_p[:, :, 0] != 7) & (textregion_pre_p[:, :, 0] != 4) & (textregion_pre_p[:, :, 0] != 2)] = 1
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return textregion_pre_p
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def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
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regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)
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##meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
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##first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
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##last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
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##last_nonzero=len(regions_without_seperators_0)-last_nonzero
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y=regions_without_seperators_0#[first_nonzero:last_nonzero]
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##y_help=np.zeros(len(y)+20)
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##y_help[10:len(y)+10]=y
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##x=np.array( range(len(y)) )
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##zneg_rev=-y_help+np.max(y_help)
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##zneg=np.zeros(len(zneg_rev)+20)
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##zneg[10:len(zneg_rev)+10]=zneg_rev
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z=gaussian_filter1d(y, sigma_)
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###zneg= gaussian_filter1d(zneg, sigma_)
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###peaks_neg, _ = find_peaks(zneg, height=0)
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###peaks, _ = find_peaks(z, height=0)
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###peaks_neg=peaks_neg-10-10
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####print(np.std(z),'np.std(z)np.std(z)np.std(z)')
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#####plt.plot(z)
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#####plt.show()
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#####plt.imshow(regions_without_seperators)
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#####plt.show()
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###"""
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###last_nonzero=last_nonzero-0#100
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###first_nonzero=first_nonzero+0#+100
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###peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
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###peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
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###"""
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###interest_pos=z[peaks]
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###interest_pos=interest_pos[interest_pos>10]
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###interest_neg=z[peaks_neg]
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###min_peaks_pos=np.mean(interest_pos)
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###min_peaks_neg=0#np.min(interest_neg)
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###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
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####print(interest_pos)
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###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
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###interest_neg_fin=interest_neg[(interest_neg<grenze)]
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###peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
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###interest_neg_fin=interest_neg[(interest_neg<grenze)]
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###"""
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###if interest_neg[0]<0.1:
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###interest_neg=interest_neg[1:]
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###if interest_neg[len(interest_neg)-1]<0.1:
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###interest_neg=interest_neg[:len(interest_neg)-1]
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###min_peaks_pos=np.min(interest_pos)
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###min_peaks_neg=0#np.min(interest_neg)
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###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
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###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
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###"""
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####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
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####peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
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####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
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###num_col=(len(interest_neg_fin))+1
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###p_l=0
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###p_u=len(y)-1
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###p_m=int(len(y)/2.)
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###p_g_l=int(len(y)/3.)
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###p_g_u=len(y)-int(len(y)/3.)
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###diff_peaks=np.abs( np.diff(peaks_neg_fin) )
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###diff_peaks_annormal=diff_peaks[diff_peaks<30]
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#print(len(interest_neg_fin),np.mean(interest_neg_fin))
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return np.std(z)#interest_neg_fin,np.std(z)
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def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
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# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
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arg_min_hor_sort = np.argsort(x_min_hor_some)
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x_min_hor_some_sort = np.sort(x_min_hor_some)
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x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
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arg_minmax = np.array(range(len(peaks_neg_fin_t)))
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indexer_lines = []
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indexes_to_delete = []
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indexer_lines_deletions_len = []
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indexr_uniq_ind = []
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for i in range(len(x_min_hor_some_sort)):
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min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
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max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
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min_h[0] = min_h[0] # +20
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max_h[len(max_h) - 1] = max_h[len(max_h) - 1] - 20
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min_h_neg = arg_minmax[(min_h < 0)]
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min_h_neg_n = min_h[min_h < 0]
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try:
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min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
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except:
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min_h_neg = []
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max_h_neg = arg_minmax[(max_h > 0)]
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max_h_neg_n = max_h[max_h > 0]
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if len(max_h_neg_n) > 0:
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max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
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else:
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max_h_neg = []
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if len(min_h_neg) > 0 and len(max_h_neg) > 0:
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deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
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unique_delets_int = []
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# print(deletions,len(deletions),'delii')
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if len(deletions) > 0:
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for j in range(len(deletions)):
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indexes_to_delete.append(deletions[j])
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# print(deletions,indexes_to_delete,'badiii')
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unique_delets = np.unique(indexes_to_delete)
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# print(min_h_neg[0],unique_delets)
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unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
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indexer_lines_deletions_len.append(len(deletions))
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indexr_uniq_ind.append([deletions])
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else:
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indexer_lines_deletions_len.append(0)
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indexr_uniq_ind.append(-999)
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index_line_true = min_h_neg[0] - len(unique_delets_int)
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# print(index_line_true)
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if index_line_true > 0 and min_h_neg[0] >= 2:
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index_line_true = index_line_true
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else:
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index_line_true = min_h_neg[0]
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indexer_lines.append(index_line_true)
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if len(unique_delets_int) > 0:
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for dd in range(len(unique_delets_int)):
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indexes_to_delete.append(unique_delets_int[dd])
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else:
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indexer_lines.append(-999)
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indexer_lines_deletions_len.append(-999)
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indexr_uniq_ind.append(-999)
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peaks_true = []
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for m in range(len(peaks_neg_fin_t)):
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if m in indexes_to_delete:
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pass
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else:
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peaks_true.append(peaks_neg_fin_t[m])
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return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
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def find_num_col(regions_without_seperators, multiplier=3.8):
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regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
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##plt.plot(regions_without_seperators_0)
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##plt.show()
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sigma_ = 35 # 70#35
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meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
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first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
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last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
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# print(last_nonzero)
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# print(isNaN(last_nonzero))
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# last_nonzero=0#halalikh
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last_nonzero = len(regions_without_seperators_0) - last_nonzero
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y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
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y_help = np.zeros(len(y) + 20)
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y_help[10 : len(y) + 10] = y
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x = np.array(range(len(y)))
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zneg_rev = -y_help + np.max(y_help)
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zneg = np.zeros(len(zneg_rev) + 20)
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zneg[10 : len(zneg_rev) + 10] = zneg_rev
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z = gaussian_filter1d(y, sigma_)
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zneg = gaussian_filter1d(zneg, sigma_)
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peaks_neg, _ = find_peaks(zneg, height=0)
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peaks, _ = find_peaks(z, height=0)
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peaks_neg = peaks_neg - 10 - 10
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last_nonzero = last_nonzero - 100
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first_nonzero = first_nonzero + 200
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peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
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peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])]
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peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))]
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# print(peaks)
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interest_pos = z[peaks]
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interest_pos = interest_pos[interest_pos > 10]
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# plt.plot(z)
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# plt.show()
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interest_neg = z[peaks_neg]
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min_peaks_pos = np.min(interest_pos)
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max_peaks_pos = np.max(interest_pos)
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if max_peaks_pos / min_peaks_pos >= 35:
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min_peaks_pos = np.mean(interest_pos)
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min_peaks_neg = 0 # np.min(interest_neg)
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# print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
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# $print(min_peaks_pos)
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dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
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# print(interest_pos)
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grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
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# print(interest_neg,'interest_neg')
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# print(grenze,'grenze')
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# print(min_peaks_pos,'min_peaks_pos')
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# print(dis_talaei,'dis_talaei')
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# print(peaks_neg,'peaks_neg')
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interest_neg_fin = interest_neg[(interest_neg < grenze)]
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peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
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# interest_neg_fin=interest_neg[(interest_neg<grenze)]
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num_col = (len(interest_neg_fin)) + 1
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# print(peaks_neg_fin,'peaks_neg_fin')
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# print(num_col,'diz')
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p_l = 0
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p_u = len(y) - 1
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p_m = int(len(y) / 2.0)
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p_g_l = int(len(y) / 4.0)
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p_g_u = len(y) - int(len(y) / 4.0)
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if num_col == 3:
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if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m):
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num_col = 1
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peaks_neg_fin = []
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else:
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pass
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if num_col == 2:
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if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
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num_col = 1
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peaks_neg_fin = []
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else:
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pass
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##print(len(peaks_neg_fin))
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diff_peaks = np.abs(np.diff(peaks_neg_fin))
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cut_off = 400
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peaks_neg_true = []
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forest = []
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# print(len(peaks_neg_fin),'len_')
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for i in range(len(peaks_neg_fin)):
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if i == 0:
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forest.append(peaks_neg_fin[i])
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if i < (len(peaks_neg_fin) - 1):
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if diff_peaks[i] <= cut_off:
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forest.append(peaks_neg_fin[i + 1])
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if diff_peaks[i] > cut_off:
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# print(forest[np.argmin(z[forest]) ] )
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if not isNaN(forest[np.argmin(z[forest])]):
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peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if i == (len(peaks_neg_fin) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
num_col = (len(peaks_neg_true)) + 1
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_quarter = int(len(y) / 5.0)
|
|
p_g_l = int(len(y) / 4.0)
|
|
p_g_u = len(y) - int(len(y) / 4.0)
|
|
|
|
p_u_quarter = len(y) - p_quarter
|
|
|
|
##print(num_col,'early')
|
|
if num_col == 3:
|
|
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and (peaks_neg_true[1] + 200) < p_m) or ((peaks_neg_true[0] - 200) > p_m and peaks_neg_true[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
|
|
peaks_neg_true = [peaks_neg_true[0]]
|
|
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
|
|
peaks_neg_true = [peaks_neg_true[1]]
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
else:
|
|
pass
|
|
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
|
|
|
|
if len(diff_peaks_annormal) > 0:
|
|
arg_help = np.array(range(len(diff_peaks)))
|
|
arg_help_ann = arg_help[diff_peaks < 360]
|
|
|
|
peaks_neg_fin_new = []
|
|
|
|
for ii in range(len(peaks_neg_fin)):
|
|
if ii in arg_help_ann:
|
|
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
|
|
if arg_min == 0:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
|
|
|
|
elif (ii - 1) in arg_help_ann:
|
|
pass
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new = peaks_neg_fin
|
|
|
|
# plt.plot(gaussian_filter1d(y, sigma_))
|
|
# plt.plot(peaks_neg_true,z[peaks_neg_true],'*')
|
|
# plt.plot([0,len(y)], [grenze,grenze])
|
|
# plt.show()
|
|
|
|
##print(len(peaks_neg_true))
|
|
return len(peaks_neg_true), peaks_neg_true
|
|
|
|
def find_num_col_only_image(regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 15
|
|
|
|
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
|
|
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
|
|
|
last_nonzero = len(regions_without_seperators_0) - last_nonzero
|
|
|
|
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
|
|
|
|
y_help = np.zeros(len(y) + 20)
|
|
|
|
y_help[10 : len(y) + 10] = y
|
|
|
|
x = np.array(range(len(y)))
|
|
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 20)
|
|
|
|
zneg[10 : len(zneg_rev) + 10] = zneg_rev
|
|
|
|
z = gaussian_filter1d(y, sigma_)
|
|
zneg = gaussian_filter1d(zneg, sigma_)
|
|
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
peaks_neg = peaks_neg - 10 - 10
|
|
|
|
peaks_neg_org = np.copy(peaks_neg)
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
|
|
|
|
peaks = peaks[(peaks > 0.09 * regions_without_seperators.shape[1]) & (peaks < 0.91 * regions_without_seperators.shape[1])]
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > 500) & (peaks_neg < (regions_without_seperators.shape[1] - 500))]
|
|
# print(peaks)
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
min_peaks_pos = np.mean(interest_pos) # np.min(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
# $print(min_peaks_pos)
|
|
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
|
|
# print(interest_pos)
|
|
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
|
|
interest_neg_fin = interest_neg[(interest_neg < grenze)]
|
|
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
|
|
|
|
num_col = (len(interest_neg_fin)) + 1
|
|
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or (peaks_neg_fin[0] < p_m and peaks_neg_fin[1] < p_m) or (peaks_neg_fin[0] > p_m and peaks_neg_fin[1] > p_m):
|
|
num_col = 1
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
|
|
num_col = 1
|
|
else:
|
|
pass
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
|
|
cut_off = 400
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks_neg_fin)):
|
|
if i == 0:
|
|
forest.append(peaks_neg_fin[i])
|
|
if i < (len(peaks_neg_fin) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if i == (len(peaks_neg_fin) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
num_col = (len(peaks_neg_true)) + 1
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_quarter = int(len(y) / 4.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
p_u_quarter = len(y) - p_quarter
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and peaks_neg_true[1] < p_m) or (peaks_neg_true[0] > p_m and peaks_neg_true[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
|
|
peaks_neg_true = [peaks_neg_true[0]]
|
|
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
|
|
peaks_neg_true = [peaks_neg_true[1]]
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
|
|
if num_col == 4:
|
|
if len(np.array(peaks_neg_true)[np.array(peaks_neg_true) < p_g_l]) == 2 or len(np.array(peaks_neg_true)[np.array(peaks_neg_true) > (len(y) - p_g_l)]) == 2:
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
else:
|
|
pass
|
|
|
|
# no deeper hill around found hills
|
|
|
|
peaks_fin_true = []
|
|
for i in range(len(peaks_neg_true)):
|
|
hill_main = peaks_neg_true[i]
|
|
# deep_depth=z[peaks_neg]
|
|
hills_around = peaks_neg_org[((peaks_neg_org > hill_main) & (peaks_neg_org <= hill_main + 400)) | ((peaks_neg_org < hill_main) & (peaks_neg_org >= hill_main - 400))]
|
|
deep_depth_around = z[hills_around]
|
|
|
|
# print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo')
|
|
try:
|
|
if np.min(deep_depth_around) < z[hill_main]:
|
|
pass
|
|
else:
|
|
peaks_fin_true.append(hill_main)
|
|
except:
|
|
pass
|
|
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
|
|
|
|
if len(diff_peaks_annormal) > 0:
|
|
arg_help = np.array(range(len(diff_peaks)))
|
|
arg_help_ann = arg_help[diff_peaks < 360]
|
|
|
|
peaks_neg_fin_new = []
|
|
|
|
for ii in range(len(peaks_neg_fin)):
|
|
if ii in arg_help_ann:
|
|
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
|
|
if arg_min == 0:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
|
|
|
|
elif (ii - 1) in arg_help_ann:
|
|
pass
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new = peaks_neg_fin
|
|
|
|
# sometime pages with one columns gives also some negative peaks. delete those peaks
|
|
param = z[peaks_neg_true] / float(min_peaks_pos) * 100
|
|
|
|
if len(param[param <= 41]) == 0:
|
|
peaks_neg_true = []
|
|
|
|
return len(peaks_fin_true), peaks_fin_true
|
|
|
|
def find_num_col_by_vertical_lines(regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 35 # 70#35
|
|
|
|
z = gaussian_filter1d(regions_without_seperators_0, sigma_)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
# print(peaks,'peaksnew')
|
|
return peaks
|
|
|
|
|
|
def delete_seperator_around(spliter_y, peaks_neg, image_by_region):
|
|
# format of subboxes box=[x1, x2 , y1, y2]
|
|
|
|
if len(image_by_region.shape) == 3:
|
|
for i in range(len(spliter_y) - 1):
|
|
for j in range(1, len(peaks_neg[i]) - 1):
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 6] = 0
|
|
image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 6] = 0
|
|
image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 6] = 0
|
|
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 7] = 0
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 7] = 0
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 7] = 0
|
|
else:
|
|
for i in range(len(spliter_y) - 1):
|
|
for j in range(1, len(peaks_neg[i]) - 1):
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 6] = 0
|
|
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 7] = 0
|
|
return image_by_region
|
|
|
|
def return_regions_without_seperators(regions_pre):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0)) * 1
|
|
# regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
|
|
|
|
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
|
|
|
regions_without_seperators = cv2.erode(regions_without_seperators, kernel, iterations=6)
|
|
|
|
return regions_without_seperators
|
|
|
|
|
|
def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
|
|
|
|
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
|
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
# boxes.append([int(x), int(y), int(w), int(h)])
|
|
|
|
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
|
|
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
|
|
|
|
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
|
|
contours_drop_parent_final.append(contours_drop_parent[jj])
|
|
|
|
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
|
|
|
|
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
|
|
|
|
return layout_no_patch
|
|
|
|
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
|
|
|
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
|
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
|
|
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areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
layout_in_patch[y : y + h, x : x + w, 0] = 4
|
|
|
|
return layout_in_patch
|
|
|
|
def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
|
|
#text_only=(regions_model_1[:,:]==1)*1
|
|
#contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
|
|
|
|
"""
|
|
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
|
|
|
|
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
|
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
|
|
|
|
###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))])
|
|
###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1])
|
|
|
|
###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002]
|
|
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
|
|
"""
|
|
|
|
cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=self.find_new_features_of_contoures(contours_only_text_parent)
|
|
|
|
length_con=x_max_main-x_min_main
|
|
height_con=y_max_main-y_min_main
|
|
|
|
|
|
|
|
all_found_texline_polygons_main=[]
|
|
all_found_texline_polygons_head=[]
|
|
|
|
all_box_coord_main=[]
|
|
all_box_coord_head=[]
|
|
|
|
slopes_main=[]
|
|
slopes_head=[]
|
|
|
|
contours_only_text_parent_main=[]
|
|
contours_only_text_parent_head=[]
|
|
|
|
contours_only_text_parent_main_d=[]
|
|
contours_only_text_parent_head_d=[]
|
|
|
|
for ii in range(len(contours_only_text_parent)):
|
|
con=contours_only_text_parent[ii]
|
|
img=np.zeros((regions_model_1.shape[0],regions_model_1.shape[1],3))
|
|
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
|
|
|
|
|
|
|
|
all_pixels=((img[:,:,0]==255)*1).sum()
|
|
|
|
pixels_header=( ( (img[:,:,0]==255) & (regions_model_full[:,:,0]==2) )*1 ).sum()
|
|
pixels_main=all_pixels-pixels_header
|
|
|
|
|
|
if (pixels_header>=pixels_main) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
|
|
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
|
|
contours_only_text_parent_head.append(con)
|
|
if contours_only_text_parent_d_ordered is not None:
|
|
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
|
|
all_box_coord_head.append(all_box_coord[ii])
|
|
slopes_head.append(slopes[ii])
|
|
all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
|
|
else:
|
|
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1
|
|
contours_only_text_parent_main.append(con)
|
|
if contours_only_text_parent_d_ordered is not None:
|
|
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
|
|
all_box_coord_main.append(all_box_coord[ii])
|
|
slopes_main.append(slopes[ii])
|
|
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
|
|
|
|
#print(all_pixels,pixels_main,pixels_header)
|
|
|
|
|
|
|
|
#plt.imshow(img[:,:,0])
|
|
#plt.show()
|
|
return regions_model_1,contours_only_text_parent_main,contours_only_text_parent_head,all_box_coord_main,all_box_coord_head,all_found_texline_polygons_main,all_found_texline_polygons_head,slopes_main,slopes_head,contours_only_text_parent_main_d,contours_only_text_parent_head_d
|
|
|
|
def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col):
|
|
# print(textlines_con)
|
|
# textlines_con=textlines_con.astype(np.uint32)
|
|
|
|
textlines_con_changed = []
|
|
for m1 in range(len(textlines_con)):
|
|
|
|
# textlines_tot=textlines_con[m1]
|
|
# textlines_tot=textlines_tot.astype()
|
|
textlines_tot = []
|
|
textlines_tot_org_form = []
|
|
# print(textlines_tot)
|
|
|
|
for nn in range(len(textlines_con[m1])):
|
|
textlines_tot.append(np.array(textlines_con[m1][nn], dtype=np.int32))
|
|
textlines_tot_org_form.append(textlines_con[m1][nn])
|
|
|
|
##img_text_all=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
|
|
##img_text_all=cv2.fillPoly(img_text_all, pts =textlines_tot , color=(1,1,1))
|
|
|
|
##plt.imshow(img_text_all)
|
|
##plt.show()
|
|
areas_cnt_text = np.array([cv2.contourArea(textlines_tot[j]) for j in range(len(textlines_tot))])
|
|
areas_cnt_text = areas_cnt_text / float(textline_iamge.shape[0] * textline_iamge.shape[1])
|
|
indexes_textlines = np.array(range(len(textlines_tot)))
|
|
|
|
# print(areas_cnt_text,np.min(areas_cnt_text),np.max(areas_cnt_text))
|
|
if num_col == 0:
|
|
min_area = 0.0004
|
|
elif num_col == 1:
|
|
min_area = 0.0003
|
|
else:
|
|
min_area = 0.0001
|
|
indexes_textlines_small = indexes_textlines[areas_cnt_text < min_area]
|
|
|
|
# print(indexes_textlines)
|
|
|
|
textlines_small = []
|
|
textlines_small_org_form = []
|
|
for i in indexes_textlines_small:
|
|
textlines_small.append(textlines_tot[i])
|
|
textlines_small_org_form.append(textlines_tot_org_form[i])
|
|
|
|
textlines_big = []
|
|
textlines_big_org_form = []
|
|
for i in list(set(indexes_textlines) - set(indexes_textlines_small)):
|
|
textlines_big.append(textlines_tot[i])
|
|
textlines_big_org_form.append(textlines_tot_org_form[i])
|
|
|
|
img_textline_s = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=(1, 1, 1))
|
|
|
|
img_textline_b = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=(1, 1, 1))
|
|
|
|
sum_small_big_all = img_textline_s + img_textline_b
|
|
sum_small_big_all2 = (sum_small_big_all[:, :] == 2) * 1
|
|
|
|
sum_intersection_sb = sum_small_big_all2.sum(axis=1).sum()
|
|
|
|
if sum_intersection_sb > 0:
|
|
|
|
dis_small_from_bigs_tot = []
|
|
for z1 in range(len(textlines_small)):
|
|
# print(len(textlines_small),'small')
|
|
intersections = []
|
|
for z2 in range(len(textlines_big)):
|
|
img_text = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=(1, 1, 1))
|
|
|
|
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=(1, 1, 1))
|
|
|
|
sum_small_big = img_text2 + img_text
|
|
sum_small_big_2 = (sum_small_big[:, :] == 2) * 1
|
|
|
|
sum_intersection = sum_small_big_2.sum(axis=1).sum()
|
|
|
|
# print(sum_intersection)
|
|
|
|
intersections.append(sum_intersection)
|
|
|
|
if len(np.array(intersections)[np.array(intersections) > 0]) == 0:
|
|
intersections = []
|
|
|
|
try:
|
|
dis_small_from_bigs_tot.append(np.argmax(intersections))
|
|
except:
|
|
dis_small_from_bigs_tot.append(-1)
|
|
|
|
smalls_list = np.array(dis_small_from_bigs_tot)[np.array(dis_small_from_bigs_tot) >= 0]
|
|
|
|
# index_small_textlines_rest=list( set(indexes_textlines_small)-set(smalls_list) )
|
|
|
|
textlines_big_with_change = []
|
|
textlines_big_with_change_con = []
|
|
textlines_small_with_change = []
|
|
|
|
for z in list(set(smalls_list)):
|
|
index_small_textlines = list(np.where(np.array(dis_small_from_bigs_tot) == z)[0])
|
|
# print(z,index_small_textlines)
|
|
|
|
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1], 3))
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=(255, 255, 255))
|
|
|
|
textlines_big_with_change.append(z)
|
|
|
|
for k in index_small_textlines:
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=(255, 255, 255))
|
|
textlines_small_with_change.append(k)
|
|
|
|
img_text2 = img_text2.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
cont, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(cont[0],type(cont))
|
|
|
|
textlines_big_with_change_con.append(cont)
|
|
textlines_big_org_form[z] = cont[0]
|
|
|
|
# plt.imshow(img_text2)
|
|
# plt.show()
|
|
|
|
# print(textlines_big_with_change,'textlines_big_with_change')
|
|
# print(textlines_small_with_change,'textlines_small_with_change')
|
|
# print(textlines_big)
|
|
textlines_con_changed.append(textlines_big_org_form)
|
|
|
|
else:
|
|
textlines_con_changed.append(textlines_big_org_form)
|
|
return textlines_con_changed
|
|
|
|
def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
|
|
indexes_sorted = np.array(indexes_sorted)
|
|
index_of_types = np.array(index_of_types)
|
|
kind_of_texts = np.array(kind_of_texts)
|
|
|
|
id_of_texts = []
|
|
order_of_texts = []
|
|
|
|
index_of_types_1 = index_of_types[kind_of_texts == 1]
|
|
indexes_sorted_1 = indexes_sorted[kind_of_texts == 1]
|
|
|
|
index_of_types_2 = index_of_types[kind_of_texts == 2]
|
|
indexes_sorted_2 = indexes_sorted[kind_of_texts == 2]
|
|
|
|
##print(index_of_types,'index_of_types')
|
|
##print(kind_of_texts,'kind_of_texts')
|
|
##print(len(found_polygons_text_region),'found_polygons_text_region')
|
|
##print(index_of_types_1,'index_of_types_1')
|
|
##print(indexes_sorted_1,'indexes_sorted_1')
|
|
index_b = 0 + ref_point
|
|
for mm in range(len(found_polygons_text_region)):
|
|
|
|
id_of_texts.append("r" + str(index_b))
|
|
interest = indexes_sorted_1[indexes_sorted_1 == index_of_types_1[mm]]
|
|
|
|
if len(interest) > 0:
|
|
order_of_texts.append(interest[0])
|
|
index_b += 1
|
|
else:
|
|
pass
|
|
|
|
for mm in range(len(found_polygons_text_region_h)):
|
|
id_of_texts.append("r" + str(index_b))
|
|
interest = indexes_sorted_2[index_of_types_2[mm]]
|
|
order_of_texts.append(interest)
|
|
index_b += 1
|
|
|
|
return order_of_texts, id_of_texts
|
|
|
|
def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
|
|
|
|
##plt.imshow(textline_mask)
|
|
##plt.show()
|
|
"""
|
|
print(len(contours_main),'contours_main')
|
|
mada_n=textline_mask.sum(axis=1)
|
|
y=mada_n[:]
|
|
|
|
y_help=np.zeros(len(y)+40)
|
|
y_help[20:len(y)+20]=y
|
|
x=np.array( range(len(y)) )
|
|
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
##plt.imshow(textline_mask[:,:])
|
|
##plt.show()
|
|
|
|
|
|
sigma_gaus=8
|
|
|
|
z= gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev=-y_help+np.max(y_help)
|
|
|
|
zneg=np.zeros(len(zneg_rev)+40)
|
|
zneg[20:len(zneg_rev)+20]=zneg_rev
|
|
zneg= gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg=peaks_neg-20-20
|
|
peaks=peaks-20
|
|
"""
|
|
|
|
textline_sum_along_width = textline_mask.sum(axis=1)
|
|
|
|
y = textline_sum_along_width[:]
|
|
y_padded = np.zeros(len(y) + 40)
|
|
y_padded[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
sigma_gaus = 8
|
|
|
|
z = gaussian_filter1d(y_padded, sigma_gaus)
|
|
zneg_rev = -y_padded + np.max(y_padded)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
##plt.plot(z)
|
|
##plt.show()
|
|
|
|
if contours_main != None:
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
|
|
if len(contours_header) != None:
|
|
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
|
|
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
|
|
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
|
|
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
|
|
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
# print(cy_main,'mainy')
|
|
|
|
peaks_neg_new = []
|
|
|
|
peaks_neg_new.append(0 + y_ref)
|
|
for iii in range(len(peaks_neg)):
|
|
peaks_neg_new.append(peaks_neg[iii] + y_ref)
|
|
|
|
peaks_neg_new.append(textline_mask.shape[0] + y_ref)
|
|
|
|
if len(cy_main) > 0 and np.max(cy_main) > np.max(peaks_neg_new):
|
|
cy_main = np.array(cy_main) * (np.max(peaks_neg_new) / np.max(cy_main)) - 10
|
|
|
|
if contours_main != None:
|
|
indexer_main = np.array(range(len(contours_main)))
|
|
|
|
if contours_main != None:
|
|
len_main = len(contours_main)
|
|
else:
|
|
len_main = 0
|
|
|
|
matrix_of_orders = np.zeros((len(contours_main) + len(contours_header), 5))
|
|
|
|
matrix_of_orders[:, 0] = np.array(range(len(contours_main) + len(contours_header)))
|
|
|
|
matrix_of_orders[: len(contours_main), 1] = 1
|
|
matrix_of_orders[len(contours_main) :, 1] = 2
|
|
|
|
matrix_of_orders[: len(contours_main), 2] = cx_main
|
|
matrix_of_orders[len(contours_main) :, 2] = cx_header
|
|
|
|
matrix_of_orders[: len(contours_main), 3] = cy_main
|
|
matrix_of_orders[len(contours_main) :, 3] = cy_header
|
|
|
|
matrix_of_orders[: len(contours_main), 4] = np.array(range(len(contours_main)))
|
|
matrix_of_orders[len(contours_main) :, 4] = np.array(range(len(contours_header)))
|
|
|
|
# print(peaks_neg_new,'peaks_neg_new')
|
|
|
|
# print(matrix_of_orders,'matrix_of_orders')
|
|
# print(peaks_neg_new,np.max(peaks_neg_new))
|
|
final_indexers_sorted = []
|
|
final_types = []
|
|
final_index_type = []
|
|
for i in range(len(peaks_neg_new) - 1):
|
|
top = peaks_neg_new[i]
|
|
down = peaks_neg_new[i + 1]
|
|
|
|
# print(top,down,'topdown')
|
|
|
|
indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
|
|
# print(top,down)
|
|
# print(cys_in,'cyyyins')
|
|
# print(indexes_in,'indexes')
|
|
sorted_inside = np.argsort(cxs_in)
|
|
|
|
ind_in_int = indexes_in[sorted_inside]
|
|
ind_in_type = types_of_text[sorted_inside]
|
|
ind_ind_type = index_types_of_text[sorted_inside]
|
|
|
|
for j in range(len(ind_in_int)):
|
|
final_indexers_sorted.append(int(ind_in_int[j]))
|
|
final_types.append(int(ind_in_type[j]))
|
|
final_index_type.append(int(ind_ind_type[j]))
|
|
|
|
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
|
|
|
|
# print(peaks_neg_new,'peaks')
|
|
# print(final_indexers_sorted,'indexsorted')
|
|
# print(final_types,'types')
|
|
# print(final_index_type,'final_index_type')
|
|
|
|
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
|
|
|
|
def implent_law_head_main_not_parallel(text_regions):
|
|
# print(text_regions.shape)
|
|
text_indexes = [1, 2] # 1: main text , 2: header , 3: comments
|
|
|
|
for t_i in text_indexes:
|
|
textline_mask = text_regions[:, :] == t_i
|
|
textline_mask = textline_mask * 255.0
|
|
|
|
textline_mask = textline_mask.astype(np.uint8)
|
|
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2)
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
# print(type(textline_mask),np.unique(textline_mask),textline_mask.shape)
|
|
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
if t_i == 1:
|
|
contours_main, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
# print(type(contours_main))
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
# print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0])
|
|
elif t_i == 2:
|
|
contours_header, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
# print(type(contours_header))
|
|
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
|
|
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
|
|
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
|
|
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
|
|
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
|
|
args = np.array(range(1, len(cy_header) + 1))
|
|
args_main = np.array(range(1, len(cy_main) + 1))
|
|
for jj in range(len(contours_main)):
|
|
headers_in_main = [(cy_header > y_min_main[jj]) & ((cy_header < y_max_main[jj]))]
|
|
mains_in_main = [(cy_main > y_min_main[jj]) & ((cy_main < y_max_main[jj]))]
|
|
args_log = args * headers_in_main
|
|
res = args_log[args_log > 0]
|
|
res_true = res - 1
|
|
|
|
args_log_main = args_main * mains_in_main
|
|
res_main = args_log_main[args_log_main > 0]
|
|
res_true_main = res_main - 1
|
|
|
|
if len(res_true) > 0:
|
|
sum_header = np.sum(areas_header[res_true])
|
|
sum_main = np.sum(areas_main[res_true_main])
|
|
if sum_main > sum_header:
|
|
cnt_int = [contours_header[j] for j in res_true]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
|
|
else:
|
|
cnt_int = [contours_main[j] for j in res_true_main]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
|
|
|
|
for jj in range(len(contours_header)):
|
|
main_in_header = [(cy_main > y_min_header[jj]) & ((cy_main < y_max_header[jj]))]
|
|
header_in_header = [(cy_header > y_min_header[jj]) & ((cy_header < y_max_header[jj]))]
|
|
args_log = args_main * main_in_header
|
|
res = args_log[args_log > 0]
|
|
res_true = res - 1
|
|
|
|
args_log_header = args * header_in_header
|
|
res_header = args_log_header[args_log_header > 0]
|
|
res_true_header = res_header - 1
|
|
|
|
if len(res_true) > 0:
|
|
|
|
sum_header = np.sum(areas_header[res_true_header])
|
|
sum_main = np.sum(areas_main[res_true])
|
|
|
|
if sum_main > sum_header:
|
|
|
|
cnt_int = [contours_header[j] for j in res_true_header]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
|
|
else:
|
|
cnt_int = [contours_main[j] for j in res_true]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
|
|
|
|
return text_regions
|
|
|
|
|
|
def return_hor_spliter_by_index(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
|
|
|
|
arg_min_hor_sort = np.argsort(x_min_hor_some)
|
|
x_min_hor_some_sort = np.sort(x_min_hor_some)
|
|
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
|
|
|
|
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
|
|
indexer_lines = []
|
|
indexes_to_delete = []
|
|
indexer_lines_deletions_len = []
|
|
indexr_uniq_ind = []
|
|
for i in range(len(x_min_hor_some_sort)):
|
|
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
|
|
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
|
|
|
|
min_h[0] = min_h[0] # +20
|
|
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] ##-20
|
|
|
|
min_h_neg = arg_minmax[(min_h < 0) & (np.abs(min_h) < 360)]
|
|
max_h_neg = arg_minmax[(max_h >= 0) & (np.abs(max_h) < 360)]
|
|
|
|
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
|
|
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
|
|
unique_delets_int = []
|
|
# print(deletions,len(deletions),'delii')
|
|
if len(deletions) > 0:
|
|
# print(deletions,len(deletions),'delii2')
|
|
|
|
for j in range(len(deletions)):
|
|
indexes_to_delete.append(deletions[j])
|
|
# print(deletions,indexes_to_delete,'badiii')
|
|
unique_delets = np.unique(indexes_to_delete)
|
|
# print(min_h_neg[0],unique_delets)
|
|
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
|
|
|
|
indexer_lines_deletions_len.append(len(deletions))
|
|
indexr_uniq_ind.append([deletions])
|
|
|
|
else:
|
|
indexer_lines_deletions_len.append(0)
|
|
indexr_uniq_ind.append(-999)
|
|
|
|
index_line_true = min_h_neg[0] - len(unique_delets_int)
|
|
# print(index_line_true)
|
|
if index_line_true > 0 and min_h_neg[0] >= 2:
|
|
index_line_true = index_line_true
|
|
else:
|
|
index_line_true = min_h_neg[0]
|
|
|
|
indexer_lines.append(index_line_true)
|
|
|
|
if len(unique_delets_int) > 0:
|
|
for dd in range(len(unique_delets_int)):
|
|
indexes_to_delete.append(unique_delets_int[dd])
|
|
else:
|
|
indexer_lines.append(-999)
|
|
indexer_lines_deletions_len.append(-999)
|
|
indexr_uniq_ind.append(-999)
|
|
|
|
peaks_true = []
|
|
for m in range(len(peaks_neg_fin_t)):
|
|
if m in indexes_to_delete:
|
|
pass
|
|
else:
|
|
peaks_true.append(peaks_neg_fin_t[m])
|
|
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
|
|
|
|
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor):
|
|
|
|
# plt.imshow(img_in_hor)
|
|
# plt.show()
|
|
|
|
# img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
|
|
img_p_in_ver = img_p_in_ver.astype(np.uint8)
|
|
img_p_in_ver = np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines_ver, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines_ver, dist_x_ver, x_min_main_ver, x_max_main_ver, cy_main_ver, slope_lines_org_ver, y_min_main_ver, y_max_main_ver, cx_main_ver = find_features_of_lines(contours_lines_ver)
|
|
|
|
for i in range(len(x_min_main_ver)):
|
|
img_p_in_ver[int(y_min_main_ver[i]) : int(y_min_main_ver[i]) + 30, int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0
|
|
img_p_in_ver[int(y_max_main_ver[i]) - 30 : int(y_max_main_ver[i]), int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0
|
|
|
|
# plt.imshow(img_p_in_ver[:,:,0])
|
|
# plt.show()
|
|
img_in_hor = img_in_hor.astype(np.uint8)
|
|
img_in_hor = np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines_hor, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines_hor, dist_x_hor, x_min_main_hor, x_max_main_hor, cy_main_hor, slope_lines_org_hor, y_min_main_hor, y_max_main_hor, cx_main_hor = find_features_of_lines(contours_lines_hor)
|
|
|
|
args_hor = np.array(range(len(slope_lines_hor)))
|
|
all_args_uniq = contours_in_same_horizon(cy_main_hor)
|
|
# print(all_args_uniq,'all_args_uniq')
|
|
if len(all_args_uniq) > 0:
|
|
if type(all_args_uniq[0]) is list:
|
|
special_seperators = []
|
|
contours_new = []
|
|
for dd in range(len(all_args_uniq)):
|
|
merged_all = None
|
|
some_args = args_hor[all_args_uniq[dd]]
|
|
some_cy = cy_main_hor[all_args_uniq[dd]]
|
|
some_x_min = x_min_main_hor[all_args_uniq[dd]]
|
|
some_x_max = x_max_main_hor[all_args_uniq[dd]]
|
|
|
|
# img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
|
|
for jv in range(len(some_args)):
|
|
|
|
img_p_in = cv2.fillPoly(img_in_hor, pts=[contours_lines_hor[some_args[jv]]], color=(1, 1, 1))
|
|
img_p_in[int(np.mean(some_cy)) - 5 : int(np.mean(some_cy)) + 5, int(np.min(some_x_min)) : int(np.max(some_x_max))] = 1
|
|
|
|
sum_dis = dist_x_hor[some_args].sum()
|
|
diff_max_min_uniques = np.max(x_max_main_hor[some_args]) - np.min(x_min_main_hor[some_args])
|
|
|
|
# print( sum_dis/float(diff_max_min_uniques) ,diff_max_min_uniques/float(img_p_in_ver.shape[1]),dist_x_hor[some_args].sum(),diff_max_min_uniques,np.mean( dist_x_hor[some_args]),np.std( dist_x_hor[some_args]) )
|
|
|
|
if diff_max_min_uniques > sum_dis and ((sum_dis / float(diff_max_min_uniques)) > 0.85) and ((diff_max_min_uniques / float(img_p_in_ver.shape[1])) > 0.85) and np.std(dist_x_hor[some_args]) < (0.55 * np.mean(dist_x_hor[some_args])):
|
|
# print(dist_x_hor[some_args],dist_x_hor[some_args].sum(),np.min(x_min_main_hor[some_args]) ,np.max(x_max_main_hor[some_args]),'jalibdi')
|
|
# print(np.mean( dist_x_hor[some_args] ),np.std( dist_x_hor[some_args] ),np.var( dist_x_hor[some_args] ),'jalibdiha')
|
|
special_seperators.append(np.mean(cy_main_hor[some_args]))
|
|
|
|
else:
|
|
img_p_in = img_in_hor
|
|
special_seperators = []
|
|
else:
|
|
img_p_in = img_in_hor
|
|
special_seperators = []
|
|
|
|
img_p_in_ver[:, :, 0][img_p_in_ver[:, :, 0] == 255] = 1
|
|
# print(img_p_in_ver.shape,np.unique(img_p_in_ver[:,:,0]))
|
|
|
|
# plt.imshow(img_p_in[:,:,0])
|
|
# plt.show()
|
|
|
|
# plt.imshow(img_p_in_ver[:,:,0])
|
|
# plt.show()
|
|
sep_ver_hor = img_p_in + img_p_in_ver
|
|
# print(sep_ver_hor.shape,np.unique(sep_ver_hor[:,:,0]),'sep_ver_horsep_ver_horsep_ver_hor')
|
|
# plt.imshow(sep_ver_hor[:,:,0])
|
|
# plt.show()
|
|
|
|
sep_ver_hor_cross = (sep_ver_hor[:, :, 0] == 2) * 1
|
|
|
|
sep_ver_hor_cross = np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
|
|
sep_ver_hor_cross = sep_ver_hor_cross.astype(np.uint8)
|
|
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
cx_cross, cy_cross, _, _, _, _, _ = find_new_features_of_contoures(contours_cross)
|
|
|
|
for ii in range(len(cx_cross)):
|
|
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40, 0] = 0
|
|
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) - 40 : int(cx_cross[ii]) - 4, 0] = 0
|
|
|
|
# plt.imshow(img_p_in[:,:,0])
|
|
# plt.show()
|
|
|
|
return img_p_in[:, :, 0], special_seperators
|
|
|
|
def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
|
|
peaks_neg_tot = []
|
|
peaks_neg_tot.append(first_point)
|
|
for ii in range(len(peaks_neg_fin)):
|
|
peaks_neg_tot.append(peaks_neg_fin[ii])
|
|
peaks_neg_tot.append(last_point)
|
|
return peaks_neg_tot
|
|
|
|
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_lines, contours_h=None):
|
|
|
|
seperators_closeup = ((region_pre_p[:, :, :] == pixel_lines)) * 1
|
|
|
|
seperators_closeup[0:110, :, :] = 0
|
|
seperators_closeup[seperators_closeup.shape[0] - 150 :, :, :] = 0
|
|
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
seperators_closeup = seperators_closeup.astype(np.uint8)
|
|
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
|
|
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
|
|
|
|
##plt.imshow(seperators_closeup[:,:,0])
|
|
##plt.show()
|
|
seperators_closeup_new = np.zeros((seperators_closeup.shape[0], seperators_closeup.shape[1]))
|
|
|
|
##_,seperators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0])
|
|
seperators_closeup_n = np.copy(seperators_closeup)
|
|
|
|
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
|
|
##plt.imshow(seperators_closeup_n[:,:,0])
|
|
##plt.show()
|
|
|
|
seperators_closeup_n_binary = np.zeros((seperators_closeup_n.shape[0], seperators_closeup_n.shape[1]))
|
|
seperators_closeup_n_binary[:, :] = seperators_closeup_n[:, :, 0]
|
|
|
|
seperators_closeup_n_binary[:, :][seperators_closeup_n_binary[:, :] != 0] = 1
|
|
# seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==0]=255
|
|
# seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==-255]=0
|
|
|
|
# seperators_closeup_n_binary=(seperators_closeup_n_binary[:,:]==2)*1
|
|
|
|
# gray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
|
|
|
|
# print(np.unique(seperators_closeup_n_binary))
|
|
|
|
##plt.imshow(seperators_closeup_n_binary)
|
|
##plt.show()
|
|
|
|
# print( np.unique(gray),np.unique(seperators_closeup_n[:,:,1]) )
|
|
|
|
gray = cv2.bitwise_not(seperators_closeup_n_binary)
|
|
gray = gray.astype(np.uint8)
|
|
|
|
##plt.imshow(gray)
|
|
##plt.show()
|
|
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2)
|
|
##plt.imshow(bw[:,:])
|
|
##plt.show()
|
|
|
|
horizontal = np.copy(bw)
|
|
vertical = np.copy(bw)
|
|
|
|
cols = horizontal.shape[1]
|
|
horizontal_size = cols // 30
|
|
# Create structure element for extracting horizontal lines through morphology operations
|
|
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
|
|
# Apply morphology operations
|
|
horizontal = cv2.erode(horizontal, horizontalStructure)
|
|
horizontal = cv2.dilate(horizontal, horizontalStructure)
|
|
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
horizontal = cv2.dilate(horizontal, kernel, iterations=2)
|
|
horizontal = cv2.erode(horizontal, kernel, iterations=2)
|
|
# plt.imshow(horizontal)
|
|
# plt.show()
|
|
|
|
rows = vertical.shape[0]
|
|
verticalsize = rows // 30
|
|
# Create structure element for extracting vertical lines through morphology operations
|
|
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
|
|
# Apply morphology operations
|
|
vertical = cv2.erode(vertical, verticalStructure)
|
|
vertical = cv2.dilate(vertical, verticalStructure)
|
|
|
|
vertical = cv2.dilate(vertical, kernel, iterations=1)
|
|
# Show extracted vertical lines
|
|
|
|
horizontal, special_seperators = combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical, horizontal)
|
|
|
|
##plt.imshow(vertical)
|
|
##plt.show()
|
|
# print(vertical.shape,np.unique(vertical),'verticalvertical')
|
|
seperators_closeup_new[:, :][vertical[:, :] != 0] = 1
|
|
seperators_closeup_new[:, :][horizontal[:, :] != 0] = 1
|
|
|
|
##plt.imshow(seperators_closeup_new)
|
|
##plt.show()
|
|
##seperators_closeup_n
|
|
vertical = np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
|
|
vertical = vertical.astype(np.uint8)
|
|
|
|
##plt.plot(vertical[:,:,0].sum(axis=0))
|
|
##plt.show()
|
|
|
|
# plt.plot(vertical[:,:,0].sum(axis=1))
|
|
# plt.show()
|
|
|
|
imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line_vers, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_line_vers)
|
|
# print(slope_lines,'vertical')
|
|
args = np.array(range(len(slope_lines)))
|
|
args_ver = args[slope_lines == 1]
|
|
dist_x_ver = dist_x[slope_lines == 1]
|
|
y_min_main_ver = y_min_main[slope_lines == 1]
|
|
y_max_main_ver = y_max_main[slope_lines == 1]
|
|
x_min_main_ver = x_min_main[slope_lines == 1]
|
|
x_max_main_ver = x_max_main[slope_lines == 1]
|
|
cx_main_ver = cx_main[slope_lines == 1]
|
|
dist_y_ver = y_max_main_ver - y_min_main_ver
|
|
len_y = seperators_closeup.shape[0] / 3.0
|
|
|
|
# plt.imshow(horizontal)
|
|
# plt.show()
|
|
|
|
horizontal = np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
|
|
horizontal = horizontal.astype(np.uint8)
|
|
imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line_hors, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_line_hors)
|
|
|
|
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
|
|
args = np.array(range(len(slope_lines)))
|
|
len_x = seperators_closeup.shape[1] / 5.0
|
|
|
|
dist_y = np.abs(y_max_main - y_min_main)
|
|
|
|
args_hor = args[slope_lines == 0]
|
|
dist_x_hor = dist_x[slope_lines == 0]
|
|
y_min_main_hor = y_min_main[slope_lines == 0]
|
|
y_max_main_hor = y_max_main[slope_lines == 0]
|
|
x_min_main_hor = x_min_main[slope_lines == 0]
|
|
x_max_main_hor = x_max_main[slope_lines == 0]
|
|
dist_y_hor = dist_y[slope_lines == 0]
|
|
cy_main_hor = cy_main[slope_lines == 0]
|
|
|
|
args_hor = args_hor[dist_x_hor >= len_x / 2.0]
|
|
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x / 2.0]
|
|
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x / 2.0]
|
|
cy_main_hor = cy_main_hor[dist_x_hor >= len_x / 2.0]
|
|
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x / 2.0]
|
|
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x / 2.0]
|
|
dist_y_hor = dist_y_hor[dist_x_hor >= len_x / 2.0]
|
|
|
|
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x / 2.0]
|
|
dist_x_hor = dist_x_hor[dist_x_hor >= len_x / 2.0]
|
|
|
|
matrix_of_lines_ch = np.zeros((len(cy_main_hor) + len(cx_main_ver), 10))
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 0] = args_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 0] = args_ver
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 1] = cx_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 2] = x_min_main_hor + 50 # x_min_main_hor+150
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 2] = x_min_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 3] = x_max_main_hor - 50 # x_max_main_hor-150
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 3] = x_max_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 4] = dist_x_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 4] = dist_x_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 5] = cy_main_hor
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 6] = y_min_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 6] = y_min_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 7] = y_max_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 7] = y_max_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 8] = dist_y_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 8] = dist_y_ver
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 9] = 1
|
|
|
|
if contours_h is not None:
|
|
slope_lines_head, dist_x_head, x_min_main_head, x_max_main_head, cy_main_head, slope_lines_org_head, y_min_main_head, y_max_main_head, cx_main_head = find_features_of_lines(contours_h)
|
|
matrix_l_n = np.zeros((matrix_of_lines_ch.shape[0] + len(cy_main_head), matrix_of_lines_ch.shape[1]))
|
|
matrix_l_n[: matrix_of_lines_ch.shape[0], :] = np.copy(matrix_of_lines_ch[:, :])
|
|
args_head = np.array(range(len(cy_main_head))) + len(cy_main_hor)
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 0] = args_head
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 2] = x_min_main_head + 30
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 3] = x_max_main_head - 30
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 4] = dist_x_head
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 5] = y_min_main_head - 3 - 8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 6] = y_min_main_head - 5 - 8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 7] = y_min_main_head + 1 - 8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 8] = 4
|
|
|
|
matrix_of_lines_ch = np.copy(matrix_l_n)
|
|
|
|
# print(matrix_of_lines_ch)
|
|
|
|
"""
|
|
|
|
|
|
|
|
seperators_closeup=seperators_closeup.astype(np.uint8)
|
|
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=find_features_of_lines(contours_lines)
|
|
|
|
slope_lines_org_hor=slope_lines_org[slope_lines==0]
|
|
args=np.array( range(len(slope_lines) ))
|
|
len_x=seperators_closeup.shape[1]/4.0
|
|
|
|
args_hor=args[slope_lines==0]
|
|
dist_x_hor=dist_x[slope_lines==0]
|
|
x_min_main_hor=x_min_main[slope_lines==0]
|
|
x_max_main_hor=x_max_main[slope_lines==0]
|
|
cy_main_hor=cy_main[slope_lines==0]
|
|
|
|
args_hor=args_hor[dist_x_hor>=len_x/2.0]
|
|
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
|
|
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
|
|
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
|
|
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
|
|
|
|
|
|
slope_lines_org_hor=slope_lines_org_hor[np.abs(slope_lines_org_hor)<1.2]
|
|
slope_mean_hor=np.mean(slope_lines_org_hor)
|
|
|
|
|
|
|
|
args_ver=args[slope_lines==1]
|
|
y_min_main_ver=y_min_main[slope_lines==1]
|
|
y_max_main_ver=y_max_main[slope_lines==1]
|
|
x_min_main_ver=x_min_main[slope_lines==1]
|
|
x_max_main_ver=x_max_main[slope_lines==1]
|
|
cx_main_ver=cx_main[slope_lines==1]
|
|
dist_y_ver=y_max_main_ver-y_min_main_ver
|
|
len_y=seperators_closeup.shape[0]/3.0
|
|
|
|
|
|
|
|
print(matrix_of_lines_ch[:,8][matrix_of_lines_ch[:,9]==0],'khatlarrrr')
|
|
args_main_spliters=matrix_of_lines_ch[:,0][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
|
|
|
|
cy_main_spliters=matrix_of_lines_ch[:,5][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
|
|
"""
|
|
|
|
cy_main_spliters = cy_main_hor[(x_min_main_hor <= 0.16 * region_pre_p.shape[1]) & (x_max_main_hor >= 0.84 * region_pre_p.shape[1])]
|
|
|
|
cy_main_spliters = np.array(list(cy_main_spliters) + list(special_seperators))
|
|
|
|
if contours_h is not None:
|
|
try:
|
|
cy_main_spliters_head = cy_main_head[(x_min_main_head <= 0.16 * region_pre_p.shape[1]) & (x_max_main_head >= 0.84 * region_pre_p.shape[1])]
|
|
cy_main_spliters = np.array(list(cy_main_spliters) + list(cy_main_spliters_head))
|
|
except:
|
|
pass
|
|
args_cy_spliter = np.argsort(cy_main_spliters)
|
|
|
|
cy_main_spliters_sort = cy_main_spliters[args_cy_spliter]
|
|
|
|
spliter_y_new = []
|
|
spliter_y_new.append(0)
|
|
for i in range(len(cy_main_spliters_sort)):
|
|
spliter_y_new.append(cy_main_spliters_sort[i])
|
|
|
|
spliter_y_new.append(region_pre_p.shape[0])
|
|
|
|
spliter_y_new_diff = np.diff(spliter_y_new) / float(region_pre_p.shape[0]) * 100
|
|
|
|
args_big_parts = np.array(range(len(spliter_y_new_diff)))[spliter_y_new_diff > 22]
|
|
|
|
regions_without_seperators = return_regions_without_seperators(region_pre_p)
|
|
|
|
##print(args_big_parts,'args_big_parts')
|
|
# image_page_otsu=otsu_copy(image_page_deskewd)
|
|
# print(np.unique(image_page_otsu[:,:,0]))
|
|
# image_page_background_zero=self.image_change_background_pixels_to_zero(image_page_otsu)
|
|
|
|
length_y_threshold = regions_without_seperators.shape[0] / 4.0
|
|
|
|
num_col_fin = 0
|
|
peaks_neg_fin_fin = []
|
|
|
|
for iteils in args_big_parts:
|
|
|
|
regions_without_seperators_teil = regions_without_seperators[int(spliter_y_new[iteils]) : int(spliter_y_new[iteils + 1]), :, 0]
|
|
# image_page_background_zero_teil=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:]
|
|
|
|
# print(regions_without_seperators_teil.shape)
|
|
##plt.imshow(regions_without_seperators_teil)
|
|
##plt.show()
|
|
|
|
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=6.0)
|
|
|
|
# regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3)
|
|
#
|
|
num_col, peaks_neg_fin = find_num_col(regions_without_seperators_teil, multiplier=7.0)
|
|
|
|
if num_col > num_col_fin:
|
|
num_col_fin = num_col
|
|
peaks_neg_fin_fin = peaks_neg_fin
|
|
"""
|
|
#print(length_y_vertical_lines,length_y_threshold,'x_center_of_ver_linesx_center_of_ver_linesx_center_of_ver_lines')
|
|
if len(cx_main_ver)>0 and len( dist_y_ver[dist_y_ver>=length_y_threshold] ) >=1:
|
|
num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=6.0)
|
|
else:
|
|
#plt.imshow(image_page_background_zero_teil)
|
|
#plt.show()
|
|
#num_col, peaks_neg_fin=find_num_col_only_image(image_page_background_zero,multiplier=2.4)#2.3)
|
|
num_col, peaks_neg_fin=find_num_col_only_image(image_page_background_zero_teil,multiplier=3.4)#2.3)
|
|
|
|
print(num_col,'birda')
|
|
if num_col>0:
|
|
pass
|
|
elif num_col==0:
|
|
print(num_col,'birda2222')
|
|
num_col_regions, peaks_neg_fin_regions=find_num_col(regions_without_seperators_teil,multiplier=10.0)
|
|
if num_col_regions==0:
|
|
pass
|
|
else:
|
|
|
|
num_col=num_col_regions
|
|
peaks_neg_fin=peaks_neg_fin_regions[:]
|
|
"""
|
|
|
|
# print(num_col+1,'num colmsssssssss')
|
|
|
|
if len(args_big_parts) == 1 and (len(peaks_neg_fin_fin) + 1) < num_col_classifier:
|
|
peaks_neg_fin = find_num_col_by_vertical_lines(vertical)
|
|
peaks_neg_fin = peaks_neg_fin[peaks_neg_fin >= 500]
|
|
peaks_neg_fin = peaks_neg_fin[peaks_neg_fin <= (vertical.shape[1] - 500)]
|
|
peaks_neg_fin_fin = peaks_neg_fin[:]
|
|
|
|
# print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza')
|
|
|
|
return num_col_fin, peaks_neg_fin_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n
|
|
|
|
def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch):
|
|
boxes = []
|
|
|
|
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
|
|
# holes in the text and also finding spliter which covers more than one columns.
|
|
for i in range(len(spliter_y_new) - 1):
|
|
# print(spliter_y_new[i],spliter_y_new[i+1])
|
|
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
|
|
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
|
|
|
|
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
|
|
|
|
# check to see is there any vertical seperator to find holes.
|
|
if 1 > 0: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
|
|
|
|
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
|
|
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.plot(org_img_dichte)
|
|
##plt.show()
|
|
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
|
|
|
|
# print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'firssst')
|
|
|
|
# plt.imshow(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:])
|
|
# plt.show()
|
|
try:
|
|
num_col, peaks_neg_fin = find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin = []
|
|
|
|
# print(peaks_neg_fin,'peaks_neg_fin')
|
|
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
|
|
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
|
|
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
|
|
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
|
|
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
|
|
|
|
peaks_neg_tot = return_points_with_boundies(peaks_neg_fin, 0, regions_without_seperators[:, :].shape[1])
|
|
|
|
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
|
|
|
|
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
|
|
|
|
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
|
|
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
|
|
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
|
|
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
|
|
|
|
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
|
|
for kkk1 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
|
|
|
|
for kkk2 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
|
|
vahid_subset[kkk1, kkk2] = kkk1
|
|
else:
|
|
pass
|
|
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
|
|
|
|
# check the len of matrix if it has no length means that there is no spliter at all
|
|
|
|
if len(vahid_subset > 0):
|
|
# print('hihoo')
|
|
|
|
# find parenets args
|
|
line_int = np.zeros(vahid_subset.shape[0])
|
|
|
|
childs_id = []
|
|
arg_child = []
|
|
for li in range(vahid_subset.shape[0]):
|
|
# print(vahid_subset[:,li])
|
|
if np.all(vahid_subset[:, li] == -1):
|
|
line_int[li] = -1
|
|
else:
|
|
line_int[li] = 1
|
|
|
|
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
|
|
# helpi=[]
|
|
# for nad in range(len(childs_args_in)):
|
|
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
|
|
|
|
arg_child.append(arg_min_hor_sort_with_subset[li])
|
|
|
|
# line_int=vahid_subset[0,:]
|
|
|
|
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
|
|
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
|
|
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
|
|
|
|
cy_hor_some_sort = cy_hor_some[arg_parent]
|
|
|
|
# print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
|
|
|
|
# args_indexes=np.array(range(len(start_index_of_hor) ))
|
|
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
if tj in np.unique(start_index_of_hor_parent):
|
|
# print(cy_hor_some_sort)
|
|
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
|
|
cy_help_sort = np.sort(cy_help)
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mj in range(len(cy_help_sort)):
|
|
newest_y_spliter.append(cy_help_sort[mj])
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
else:
|
|
line_int = []
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
# if line_int is all -1 means that big spliters have no child and we can easily go through
|
|
if np.all(np.array(line_int) == -1):
|
|
for j in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
|
|
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
|
|
##plt.show()
|
|
|
|
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
|
|
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
|
|
pass
|
|
|
|
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
# print( int(newest_y_spliter[n]),int(newest_y_spliter[n+1]),newest_peaks[j],newest_peaks[j+1] )
|
|
try:
|
|
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin_sub = []
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for j in range(len(newest_peaks) - 1):
|
|
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
if j in start_index_of_hor_parent:
|
|
|
|
x_min_ch = x_min_hor_some[arg_child]
|
|
x_max_ch = x_max_hor_some[arg_child]
|
|
cy_hor_some_sort_child = cy_hor_some[arg_child]
|
|
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
|
|
|
|
if len(cy_child_in) > 0:
|
|
try:
|
|
num_col_ch, peaks_neg_ch = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_ch = []
|
|
# print(peaks_neg_ch,'mizzzz')
|
|
# peaks_neg_ch=[]
|
|
# for djh in range(len(peaks_neg_ch)):
|
|
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
|
|
|
|
peaks_neg_ch_tot = return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
|
|
|
|
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
|
|
|
|
newest_y_spliter_ch_tot = []
|
|
|
|
for tjj in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_new = []
|
|
newest_y_spliter_new.append(newest_y_spliter[n])
|
|
if tjj in np.unique(ss_in_ch):
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mjj in range(len(cy_child_in)):
|
|
newest_y_spliter_new.append(cy_child_in[mjj])
|
|
newest_y_spliter_new.append(newest_y_spliter[n + 1])
|
|
|
|
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
|
|
|
|
for jn in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
|
|
|
|
for nd in range(len(newest_y_spliter_h) - 1):
|
|
|
|
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
|
|
try:
|
|
num_col_sub_ch, peaks_neg_fin_sub_ch = find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin_sub_ch = []
|
|
|
|
else:
|
|
peaks_neg_fin_sub_ch = []
|
|
|
|
peaks_sub_ch = []
|
|
peaks_sub_ch.append(nst_p_ch[jn])
|
|
|
|
for kjj in range(len(peaks_neg_fin_sub_ch)):
|
|
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
|
|
|
|
peaks_sub_ch.append(nst_p_ch[jn + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for khh in range(len(peaks_sub_ch) - 1):
|
|
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
|
|
|
|
else:
|
|
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
try:
|
|
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin_sub = []
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
try:
|
|
num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
|
|
except:
|
|
peaks_neg_fin_sub = []
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
boxes.append([0, regions_without_seperators[:, :].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
|
|
return boxes
|
|
|