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1672 lines
72 KiB
Python
1672 lines
72 KiB
Python
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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def filter_contours_area_of_image(image, contours, hirarchy, 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 hirarchy[0][jv][3] == -1: # and hirarchy[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_interiors(image, contours, hirarchy, 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 hirarchy[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.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, hirarchy, 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(hirarchy[0][jv][3],hirarchy )
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hirarchy[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 resize_image(img_in, input_height, input_width):
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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def rotatedRectWithMaxArea(w, h, angle):
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if w <= 0 or h <= 0:
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return 0, 0
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width_is_longer = w >= h
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side_long, side_short = (w, h) if width_is_longer else (h, w)
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# since the solutions for angle, -angle and 180-angle are all the same,
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# if suffices to look at the first quadrant and the absolute values of sin,cos:
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sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
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if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
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# half constrained case: two crop corners touch the longer side,
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# the other two corners are on the mid-line parallel to the longer line
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x = 0.5 * side_short
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wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
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else:
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# fully constrained case: crop touches all 4 sides
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cos_2a = cos_a * cos_a - sin_a * sin_a
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wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
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return wr, hr
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def rotate_max_area_new(image, rotated, angle):
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wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
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h, w, _ = rotated.shape
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y1 = h // 2 - int(hr / 2)
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y2 = y1 + int(hr)
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x1 = w // 2 - int(wr / 2)
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x2 = x1 + int(wr)
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return rotated[y1:y2, x1:x2]
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def rotation_image_new(img, thetha):
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rotated = imutils.rotate(img, thetha)
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return rotate_max_area_new(img, rotated, thetha)
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def rotate_image(img_patch, slope):
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(h, w) = img_patch.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, slope, 1.0)
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return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
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def rotyate_image_different( img, slope):
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# img = cv2.imread('images/input.jpg')
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num_rows, num_cols = img.shape[:2]
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rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
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img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
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return img_rotation
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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 return_bonding_box_of_contours(cnts):
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boxes_tot = []
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for i in range(len(cnts)):
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x, y, w, h = cv2.boundingRect(cnts[i])
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box = [x, y, w, h]
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boxes_tot.append(box)
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return boxes_tot
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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 isNaN(num):
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return num != num
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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, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hiearchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area)
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return contours_imgs
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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 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, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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return contours, hierachy
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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, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hiearchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size)
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return contours_imgs
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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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# print(cnts_org,'cnts_org')
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# sys.exit()
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# self.y_shift = np.abs(img_copy.shape[0] - img.shape[0])
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# self.x_shift = np.abs(img_copy.shape[1] - img.shape[1])
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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, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hiearchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
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return contours_imgs
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def seperate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines):
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kernel = np.ones((5, 5), np.uint8)
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pixel = 255
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min_area = 0
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max_area = 1
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if len(img_patch.shape) == 3:
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cnts_images = (img_patch[:, :, 0] == pixel) * 1
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else:
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cnts_images = (img_patch[:, :] == 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, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hiearchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
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cont_final = []
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###print(add_boxes_coor_into_textlines,'ikki')
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for i in range(len(contours_imgs)):
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img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
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img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))
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img_contour = img_contour.astype(np.uint8)
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img_contour = cv2.dilate(img_contour, kernel, iterations=4)
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imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
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_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
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contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
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##0]
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##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
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##if add_boxes_coor_into_textlines:
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##print(np.shape(contours_text_rot[0]),'sjppo')
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##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0]
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##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1]
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cont_final.append(contours_text_rot[0])
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##print(cont_final,'nadizzzz')
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return None, cont_final
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def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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(h, w) = img_patch.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
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x_d = M[0, 2]
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y_d = M[1, 2]
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thetha = thetha / 180.0 * np.pi
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rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
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contour_text_interest_copy = contour_text_interest.copy()
|
|
|
|
x_cont = contour_text_interest[:, 0, 0]
|
|
y_cont = contour_text_interest[:, 0, 1]
|
|
x_cont = x_cont - np.min(x_cont)
|
|
y_cont = y_cont - np.min(y_cont)
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_patch.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_patch.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
textline_patch_sum_along_width = img_patch.sum(axis=1)
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
|
|
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)
|
|
|
|
if 1 > 0:
|
|
|
|
try:
|
|
|
|
y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
|
|
y_padded_up_to_down_e = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
|
|
y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
|
|
y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
|
|
|
|
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
|
|
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks_e[:]
|
|
peaks_neg_new = peaks_neg_e[:]
|
|
|
|
clusters_to_be_deleted = []
|
|
if len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
else:
|
|
peaks_new_tot = peaks_e[:]
|
|
|
|
textline_con, hierachy = return_contours_of_image(img_patch)
|
|
textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
|
|
# print(sigma_gaus,'sigma_gaus')
|
|
except:
|
|
sigma_gaus = 12
|
|
if sigma_gaus < 3:
|
|
sigma_gaus = 3
|
|
# print(sigma_gaus,'sigma')
|
|
|
|
y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
|
|
y_padded_up_to_down = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
|
|
y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
|
|
y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(y_padded_smoothed, height=0)
|
|
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
|
|
|
|
try:
|
|
neg_peaks_max = np.max(y_padded_smoothed[peaks])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42]
|
|
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
except:
|
|
arg_neg_must_be_deleted = []
|
|
arg_diff_cluster = []
|
|
|
|
try:
|
|
peaks_new = peaks[:]
|
|
peaks_neg_new = peaks_neg[:]
|
|
clusters_to_be_deleted = []
|
|
|
|
if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
|
|
|
|
if len(arg_neg_must_be_deleted) == 1:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
##plt.plot(y_padded_up_to_down_padded)
|
|
##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
|
|
##plt.show()
|
|
|
|
##plt.plot(y_padded_up_to_down_padded)
|
|
##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
|
|
##plt.show()
|
|
|
|
##plt.plot(y_padded_smoothed)
|
|
##plt.plot(peaks,y_padded_smoothed[peaks],'*')
|
|
##plt.show()
|
|
|
|
##plt.plot(y_padded_smoothed)
|
|
##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
|
|
##plt.show()
|
|
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
else:
|
|
peaks_new_tot = peaks[:]
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
except:
|
|
pass
|
|
|
|
mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
|
|
std_value_of_peaks = np.std(y_padded_smoothed[peaks])
|
|
peaks_values = y_padded_smoothed[peaks]
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
for jj in range(len(peaks_neg)):
|
|
if peaks_neg[jj] > len(x) - 1:
|
|
peaks_neg[jj] = len(x) - 1
|
|
|
|
for jj in range(len(peaks)):
|
|
if peaks[jj] > len(x) - 1:
|
|
peaks[jj] = len(x) - 1
|
|
|
|
textline_boxes = []
|
|
textline_boxes_rot = []
|
|
|
|
if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == (len(peaks) - 1):
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
else:
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
|
|
if point_down_narrow >= img_patch.shape[0]:
|
|
point_down_narrow = img_patch.shape[0] - 2
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
elif len(peaks) < 1:
|
|
pass
|
|
|
|
elif len(peaks) == 1:
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
# x_min = x_min_cont
|
|
# x_max = x_max_cont
|
|
|
|
y_min = y_min_cont
|
|
y_max = y_max_cont
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]]))
|
|
|
|
elif len(peaks) == 2:
|
|
dis_to_next = np.abs(peaks[1] - peaks[0])
|
|
for jj in range(len(peaks)):
|
|
if jj == 0:
|
|
point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
elif jj == 1:
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside)
|
|
x_max = np.max(xvinside)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
else:
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == 0:
|
|
dis_to_next = peaks[jj + 1] - peaks[jj]
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next)
|
|
elif jj == len(peaks) - 1:
|
|
dis_to_next = peaks[jj] - peaks[jj - 1]
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
else:
|
|
dis_to_next_down = peaks[jj + 1] - peaks[jj]
|
|
dis_to_next_up = peaks[jj] - peaks[jj - 1]
|
|
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
return peaks, textline_boxes_rot
|
|
|
|
def seperate_lines_vertical(img_patch, contour_text_interest, thetha):
|
|
|
|
thetha = thetha + 90
|
|
|
|
(h, w) = img_patch.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
contour_text_interest_copy = contour_text_interest.copy()
|
|
|
|
x_cont = contour_text_interest[:, 0, 0]
|
|
y_cont = contour_text_interest[:, 0, 1]
|
|
x_cont = x_cont - np.min(x_cont)
|
|
y_cont = y_cont - np.min(y_cont)
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_patch.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_patch.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
textline_patch_sum_along_width = img_patch.sum(axis=0)
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
|
|
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)
|
|
if 1 > 0:
|
|
|
|
try:
|
|
|
|
y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
|
|
y_padded_up_to_down_e = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
|
|
y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
|
|
y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
|
|
|
|
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
|
|
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks_e[:]
|
|
peaks_neg_new = peaks_neg_e[:]
|
|
|
|
clusters_to_be_deleted = []
|
|
if len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
else:
|
|
peaks_new_tot = peaks_e[:]
|
|
|
|
textline_con, hierachy = return_contours_of_image(img_patch)
|
|
textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
|
|
# print(sigma_gaus,'sigma_gaus')
|
|
except:
|
|
sigma_gaus = 12
|
|
if sigma_gaus < 3:
|
|
sigma_gaus = 3
|
|
# print(sigma_gaus,'sigma')
|
|
|
|
y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
|
|
y_padded_up_to_down = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
|
|
y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
|
|
y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(y_padded_smoothed, height=0)
|
|
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
|
|
|
|
# plt.plot(y_padded_up_to_down_padded)
|
|
# plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
|
|
# plt.title('negs')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_smoothed)
|
|
# plt.plot(peaks,y_padded_smoothed[peaks],'*')
|
|
# plt.title('poss')
|
|
# plt.show()
|
|
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded[peaks_neg])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42]
|
|
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks[:]
|
|
peaks_neg_new = peaks_neg[:]
|
|
clusters_to_be_deleted = []
|
|
|
|
if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
|
|
|
|
if len(arg_neg_must_be_deleted) == 1:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
else:
|
|
peaks_new_tot = peaks[:]
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
|
|
std_value_of_peaks = np.std(y_padded_smoothed[peaks])
|
|
peaks_values = y_padded_smoothed[peaks]
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
for jj in range(len(peaks_neg)):
|
|
if peaks_neg[jj] > len(x) - 1:
|
|
peaks_neg[jj] = len(x) - 1
|
|
|
|
for jj in range(len(peaks)):
|
|
if peaks[jj] > len(x) - 1:
|
|
peaks[jj] = len(x) - 1
|
|
|
|
textline_boxes = []
|
|
textline_boxes_rot = []
|
|
|
|
if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
|
|
# print('11')
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == (len(peaks) - 1):
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
else:
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
|
|
if point_down_narrow >= img_patch.shape[0]:
|
|
point_down_narrow = img_patch.shape[0] - 2
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
elif len(peaks) < 1:
|
|
pass
|
|
|
|
elif len(peaks) == 1:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
|
|
y_min = y_min_cont
|
|
y_max = y_max_cont
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]]))
|
|
|
|
elif len(peaks) == 2:
|
|
dis_to_next = np.abs(peaks[1] - peaks[0])
|
|
for jj in range(len(peaks)):
|
|
if jj == 0:
|
|
point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
elif jj == 1:
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside)
|
|
x_max = np.max(xvinside)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
else:
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == 0:
|
|
dis_to_next = peaks[jj + 1] - peaks[jj]
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next)
|
|
elif jj == len(peaks) - 1:
|
|
dis_to_next = peaks[jj] - peaks[jj - 1]
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
else:
|
|
dis_to_next_down = peaks[jj + 1] - peaks[jj]
|
|
dis_to_next_up = peaks[jj] - peaks[jj - 1]
|
|
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
return peaks, textline_boxes_rot
|
|
|
|
def seperate_lines_new_inside_teils2(img_patch, thetha):
|
|
|
|
(h, w) = img_patch.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
# contour_text_interest_copy = contour_text_interest.copy()
|
|
|
|
# x_cont = contour_text_interest[:, 0, 0]
|
|
# y_cont = contour_text_interest[:, 0, 1]
|
|
# x_cont = x_cont - np.min(x_cont)
|
|
# y_cont = y_cont - np.min(y_cont)
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_patch.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_patch.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
textline_patch_sum_along_width = img_patch.sum(axis=1)
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
|
|
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)
|
|
if 1 > 0:
|
|
|
|
try:
|
|
|
|
y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
|
|
y_padded_up_to_down_e = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
|
|
y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
|
|
y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
|
|
|
|
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
|
|
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks_e[:]
|
|
peaks_neg_new = peaks_neg_e[:]
|
|
|
|
clusters_to_be_deleted = []
|
|
if len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
else:
|
|
peaks_new_tot = peaks_e[:]
|
|
|
|
textline_con, hierachy = return_contours_of_image(img_patch)
|
|
textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
|
|
# print(sigma_gaus,'sigma_gaus')
|
|
except:
|
|
sigma_gaus = 12
|
|
if sigma_gaus < 3:
|
|
sigma_gaus = 3
|
|
# print(sigma_gaus,'sigma')
|
|
|
|
y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
|
|
y_padded_up_to_down = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
|
|
y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
|
|
y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(y_padded_smoothed, height=0)
|
|
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
|
|
|
|
peaks_new = peaks[:]
|
|
peaks_neg_new = peaks_neg[:]
|
|
|
|
try:
|
|
neg_peaks_max = np.max(y_padded_smoothed[peaks])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24]
|
|
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
clusters_to_be_deleted = []
|
|
|
|
if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
|
|
|
|
if len(arg_neg_must_be_deleted) == 1:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
# plt.plot(y_padded_up_to_down_padded)
|
|
# plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_up_to_down_padded)
|
|
# plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_smoothed)
|
|
# plt.plot(peaks,y_padded_smoothed[peaks],'*')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_smoothed)
|
|
# plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
|
|
# plt.show()
|
|
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
except:
|
|
pass
|
|
|
|
else:
|
|
peaks_new_tot = peaks[:]
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
|
|
std_value_of_peaks = np.std(y_padded_smoothed[peaks])
|
|
peaks_values = y_padded_smoothed[peaks]
|
|
|
|
###peaks_neg = peaks_neg - 20 - 20
|
|
###peaks = peaks - 20
|
|
peaks_neg_true = peaks_neg[:]
|
|
peaks_pos_true = peaks[:]
|
|
|
|
if len(peaks_neg_true) > 0:
|
|
peaks_neg_true = np.array(peaks_neg_true)
|
|
|
|
peaks_neg_true = peaks_neg_true - 20 - 20
|
|
|
|
# print(peaks_neg_true)
|
|
for i in range(len(peaks_neg_true)):
|
|
img_patch[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0
|
|
|
|
else:
|
|
pass
|
|
|
|
if len(peaks_pos_true) > 0:
|
|
peaks_pos_true = np.array(peaks_pos_true)
|
|
peaks_pos_true = peaks_pos_true - 20
|
|
|
|
for i in range(len(peaks_pos_true)):
|
|
##img_patch[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
|
|
img_patch[peaks_pos_true[i] - 6 : peaks_pos_true[i] + 6, :] = 1
|
|
else:
|
|
pass
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
# img_patch = cv2.erode(img_patch,kernel,iterations = 3)
|
|
#######################img_patch = cv2.erode(img_patch,kernel,iterations = 2)
|
|
img_patch = cv2.erode(img_patch, kernel, iterations=1)
|
|
return img_patch
|
|
|
|
def filter_small_drop_capitals_from_no_patch_layout(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] > 0.001]
|
|
|
|
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])
|
|
# boxes.append([int(x), int(y), int(w), int(h)])
|
|
|
|
iou_of_box_and_contoure = float(drop_only.shape[0] * drop_only.shape[1]) * areas_cnt_text[jj] / float(w * h) * 100
|
|
height_to_weight_ratio = h / float(w)
|
|
weigh_to_height_ratio = w / float(h)
|
|
|
|
if iou_of_box_and_contoure > 60 and weigh_to_height_ratio < 1.2 and height_to_weight_ratio < 2:
|
|
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 find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1)
|
|
|
|
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
|
|
|
|
# print(np.std(z),'np.std(z)np.std(z)np.std(z)')
|
|
|
|
##plt.plot(z)
|
|
##plt.show()
|
|
|
|
##plt.imshow(regions_without_seperators)
|
|
##plt.show()
|
|
"""
|
|
last_nonzero=last_nonzero-0#100
|
|
first_nonzero=first_nonzero+0#+100
|
|
|
|
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
|
|
|
|
peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
|
|
"""
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
|
|
min_peaks_pos = np.mean(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
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)]
|
|
interest_neg_fin = interest_neg[(interest_neg < grenze)]
|
|
|
|
"""
|
|
if interest_neg[0]<0.1:
|
|
interest_neg=interest_neg[1:]
|
|
if interest_neg[len(interest_neg)-1]<0.1:
|
|
interest_neg=interest_neg[:len(interest_neg)-1]
|
|
|
|
|
|
|
|
min_peaks_pos=np.min(interest_pos)
|
|
min_peaks_neg=0#np.min(interest_neg)
|
|
|
|
|
|
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
|
|
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)]
|
|
# interest_neg_fin=interest_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)
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
|
|
|
|
# print(len(interest_neg_fin),np.mean(interest_neg_fin))
|
|
return interest_neg_fin, np.std(z)
|
|
|
|
def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
|
|
# print(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)]
|
|
min_h_neg_n = min_h[min_h < 0]
|
|
|
|
try:
|
|
min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
|
|
except:
|
|
min_h_neg = []
|
|
|
|
max_h_neg = arg_minmax[(max_h > 0)]
|
|
max_h_neg_n = max_h[max_h > 0]
|
|
|
|
if len(max_h_neg_n) > 0:
|
|
max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
|
|
else:
|
|
max_h_neg = []
|
|
|
|
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:
|
|
|
|
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 find_new_features_of_contoures(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))]
|
|
try:
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
|
|
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] 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))])
|
|
except:
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
|
|
|
|
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
|
|
|
|
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
|
|
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
|
|
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
|
|
|
|
# dis_x=np.abs(x_max_main-x_min_main)
|
|
|
|
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
|