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376 lines
15 KiB
Python
376 lines
15 KiB
Python
import cv2
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import numpy as np
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from shapely import geometry
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from .rotate import rotate_image, rotation_image_new
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from multiprocessing import Process, Queue, cpu_count
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from multiprocessing import Pool
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def contours_in_same_horizon(cy_main_hor):
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X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
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X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
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X1[0::1, :] = cy_main_hor[:]
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X2 = X1.T
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X_dif = np.abs(X2 - X1)
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args_help = np.array(range(len(cy_main_hor)))
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all_args = []
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for i in range(len(cy_main_hor)):
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list_h = list(args_help[X_dif[i, :] <= 20])
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list_h.append(i)
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if len(list_h) > 1:
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all_args.append(list(set(list_h)))
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return np.unique(np.array(all_args, dtype=object))
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def find_contours_mean_y_diff(contours_main):
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_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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return np.mean(np.diff(np.sort(np.array(cy_main))))
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def get_text_region_boxes_by_given_contours(contours):
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kernel = np.ones((5, 5), np.uint8)
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boxes = []
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contours_new = []
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for jj in range(len(contours)):
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x, y, w, h = cv2.boundingRect(contours[jj])
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boxes.append([x, y, w, h])
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contours_new.append(contours[jj])
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del contours
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return boxes, contours_new
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def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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area = polygon.area
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hierarchy[0][jv][3] == -1: # and hierarchy[0][jv][3]==-1 :
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found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
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jv += 1
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return found_polygons_early
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def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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# area = cv2.contourArea(c)
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area = polygon.area
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##print(np.prod(thresh.shape[:2]))
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# Check that polygon has area greater than minimal area
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# print(hierarchy[0][jv][3],hierarchy )
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 :
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# print(c[0][0][1])
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found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32))
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jv += 1
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return found_polygons_early
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def find_new_features_of_contours(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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try:
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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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argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
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y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] 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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except:
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x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
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argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
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x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
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y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
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x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
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y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
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y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
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# dis_x=np.abs(x_max_main-x_min_main)
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return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
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def find_features_of_contours(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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return y_min_main, y_max_main
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def return_parent_contours(contours, hierarchy):
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contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1]
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return contours_parent
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def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
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# pixels of images are identified by 5
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if len(region_pre_p.shape) == 3:
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cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
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else:
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cnts_images = (region_pre_p[:, :] == pixel) * 1
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cnts_images = cnts_images.astype(np.uint8)
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cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
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imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hierarchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=min_area)
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return contours_imgs
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def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, indexes_r_con_per_pro, img, slope_first):
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cnts_org_per_each_subprocess = []
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index_by_text_region_contours = []
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for mv in range(len(contours_per_process)):
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index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
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img_copy = np.zeros(img.shape)
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img_copy = cv2.fillPoly(img_copy, pts=[contours_per_process[mv]], color=(1, 1, 1))
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img_copy = rotation_image_new(img_copy, -slope_first)
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img_copy = img_copy.astype(np.uint8)
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imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
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cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
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cnts_org_per_each_subprocess.append(cont_int[0])
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queue_of_all_params.put([ cnts_org_per_each_subprocess, index_by_text_region_contours])
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def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
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num_cores = cpu_count()
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queue_of_all_params = Queue()
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processes = []
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nh = np.linspace(0, len(cnts), num_cores + 1)
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indexes_by_text_con = np.array(range(len(cnts)))
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for i in range(num_cores):
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contours_per_process = cnts[int(nh[i]) : int(nh[i + 1])]
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indexes_text_con_per_process = indexes_by_text_con[int(nh[i]) : int(nh[i + 1])]
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processes.append(Process(target=do_work_of_contours_in_image, args=(queue_of_all_params, contours_per_process, indexes_text_con_per_process, img,slope_first )))
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for i in range(num_cores):
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processes[i].start()
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cnts_org = []
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all_index_text_con = []
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for i in range(num_cores):
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list_all_par = queue_of_all_params.get(True)
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contours_for_sub_process = list_all_par[0]
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indexes_for_sub_process = list_all_par[1]
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for j in range(len(contours_for_sub_process)):
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cnts_org.append(contours_for_sub_process[j])
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all_index_text_con.append(indexes_for_sub_process[j])
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for i in range(num_cores):
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processes[i].join()
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print(all_index_text_con)
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return cnts_org
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def loop_contour_image(index_l, cnts,img, slope_first):
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img_copy = np.zeros(img.shape)
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img_copy = cv2.fillPoly(img_copy, pts=[cnts[index_l]], color=(1, 1, 1))
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# plt.imshow(img_copy)
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# plt.show()
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# print(img.shape,'img')
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img_copy = rotation_image_new(img_copy, -slope_first)
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##print(img_copy.shape,'img_copy')
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# plt.imshow(img_copy)
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# plt.show()
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img_copy = img_copy.astype(np.uint8)
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imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
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cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
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# print(np.shape(cont_int[0]))
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return cont_int[0]
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def get_textregion_contours_in_org_image_multi2(cnts, img, slope_first):
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cnts_org = []
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# print(cnts,'cnts')
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with Pool(cpu_count()) as p:
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cnts_org = p.starmap(loop_contour_image, [(index_l,cnts, img,slope_first) for index_l in range(len(cnts))])
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print(len(cnts_org),'lendiha')
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return cnts_org
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def get_textregion_contours_in_org_image(cnts, img, slope_first):
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cnts_org = []
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# print(cnts,'cnts')
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for i in range(len(cnts)):
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img_copy = np.zeros(img.shape)
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img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
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# plt.imshow(img_copy)
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# plt.show()
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# print(img.shape,'img')
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img_copy = rotation_image_new(img_copy, -slope_first)
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##print(img_copy.shape,'img_copy')
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# plt.imshow(img_copy)
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# plt.show()
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img_copy = img_copy.astype(np.uint8)
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imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
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cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
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# print(np.shape(cont_int[0]))
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cnts_org.append(cont_int[0])
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return cnts_org
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def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
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h_o = img.shape[0]
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w_o = img.shape[1]
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img = cv2.resize(img, (int(img.shape[1]/3.), int(img.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
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##cnts = list( (np.array(cnts)/2).astype(np.int16) )
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#cnts = cnts/2
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cnts = [(i/ 3).astype(np.int32) for i in cnts]
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cnts_org = []
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#print(cnts,'cnts')
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for i in range(len(cnts)):
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img_copy = np.zeros(img.shape)
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img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
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# plt.imshow(img_copy)
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# plt.show()
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# print(img.shape,'img')
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img_copy = rotation_image_new(img_copy, -slope_first)
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##print(img_copy.shape,'img_copy')
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# plt.imshow(img_copy)
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# plt.show()
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img_copy = img_copy.astype(np.uint8)
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imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
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cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
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# print(np.shape(cont_int[0]))
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cnts_org.append(cont_int[0]*3)
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return cnts_org
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def return_contours_of_interested_textline(region_pre_p, pixel):
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# pixels of images are identified by 5
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if len(region_pre_p.shape) == 3:
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cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
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else:
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cnts_images = (region_pre_p[:, :] == pixel) * 1
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cnts_images = cnts_images.astype(np.uint8)
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cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
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imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hierarchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=0.000000003)
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return contours_imgs
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def return_contours_of_image(image):
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if len(image.shape) == 2:
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image = np.repeat(image[:, :, np.newaxis], 3, axis=2)
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image = image.astype(np.uint8)
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else:
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image = image.astype(np.uint8)
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imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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return contours, hierarchy
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def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003):
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# pixels of images are identified by 5
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if len(region_pre_p.shape) == 3:
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cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
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else:
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cnts_images = (region_pre_p[:, :] == pixel) * 1
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cnts_images = cnts_images.astype(np.uint8)
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cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
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imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hierarchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=min_size)
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return contours_imgs
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def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area, max_area):
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# pixels of images are identified by 5
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if len(region_pre_p.shape) == 3:
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cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
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else:
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cnts_images = (region_pre_p[:, :] == pixel) * 1
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cnts_images = cnts_images.astype(np.uint8)
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cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
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imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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|
|
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contours_imgs = return_parent_contours(contours_imgs, hierarchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=max_area, min_area=min_area)
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|
|
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img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
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img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))
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return img_ret[:, :, 0]
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