changed tolerance in adjacent1 function in postprocessing.y from 2 to 4
added function so vertical and horizontal components do not overlap the layout box of the table
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llocarnini
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57ca47f38d
commit
d70781f4aa
@ -9,6 +9,7 @@ from pdf2image import pdf2image
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from vidocp.utils.display import show_mpl
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from vidocp.utils.draw import draw_rectangles
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from vidocp.utils.post_processing import xywh_to_vecs, xywh_to_vec_rect, adjacent1d, remove_isolated
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from vidocp.layout_parsing import parse_layout
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def add_external_contours(image, img):
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@ -23,50 +24,8 @@ def add_external_contours(image, img):
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return image
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def process_lines(img_line_component):
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def draw_lines(detected_lines, img_bin):
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for line in detected_lines:
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for x1, y1, x2, y2 in line:
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cv2.line(img_bin, (x1, y1), (x2, y2), (255, 255, 255), 6)
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return img_bin
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lines = cv2.HoughLines(img_line_component, 1, np.pi / 180, 500)
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draw_lines(lines, lines)
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return img_line_component
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# def isolate_vertical_and_horizontal_components(img_bin):
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# line_min_width = 50
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# kernel_h = np.ones((1, line_min_width), np.uint8)
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# kernel_v = np.ones((line_min_width, 1), np.uint8)
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#
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# img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
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# img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
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# show_mpl(img_bin_h | img_bin_v)
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#
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# img_bin_h = apply_motion_blur(img_bin_h, 140, 0)
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# img_bin_v = apply_motion_blur(img_bin_v, 140, 90)
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# show_mpl(img_bin_h | img_bin_v)
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#
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# th1, img_bin_h = cv2.threshold(img_bin_h, 95, 255, cv2.THRESH_BINARY)
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# th1, img_bin_v = cv2.threshold(img_bin_v, 95, 255, cv2.THRESH_BINARY)
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# show_mpl(img_bin_h | img_bin_v)
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#
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# kernel_h = np.ones((1, 8), np.uint8)
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# kernel_v = np.ones((8, 1), np.uint8)
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# img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=4)
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# img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=4)
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#
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# img_bin_final = img_bin_h | img_bin_v
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# show_mpl(img_bin_final)
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# # th 130
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# #th1, img_bin_final = cv2.threshold(img_bin_final, 90, 255, cv2.THRESH_BINARY)
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# #show_mpl(img_bin_final)
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# return img_bin_final
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def isolate_vertical_and_horizontal_components(img_bin):
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line_min_width = 30
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def isolate_vertical_and_horizontal_components(img_bin, bounding_rects):
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line_min_width = 47
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kernel_h = np.ones((1, line_min_width), np.uint8)
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kernel_v = np.ones((line_min_width, 1), np.uint8)
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@ -74,51 +33,32 @@ def isolate_vertical_and_horizontal_components(img_bin):
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img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
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show_mpl(img_bin_h | img_bin_v)
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img_bin_h = apply_motion_blur(img_bin_h, 150, 0)
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img_bin_v = apply_motion_blur(img_bin_v, 150, 90)
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kernel_h = np.ones((1, 30), np.uint8)
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kernel_v = np.ones((30, 1), np.uint8)
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img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=2)
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img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=2)
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show_mpl(img_bin_h | img_bin_v)
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th1, img_bin_h = cv2.threshold(img_bin_h, 70, 255, cv2.THRESH_BINARY)
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th1, img_bin_v = cv2.threshold(img_bin_v, 70, 255, cv2.THRESH_BINARY)
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show_mpl(img_bin_h | img_bin_v)
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kernel_h = np.ones((1, 10), np.uint8)
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kernel_v = np.ones((10, 1), np.uint8)
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img_bin_h = cv2.erode(img_bin_h, kernel_h, iterations=1)
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img_bin_v = cv2.erode(img_bin_v, kernel_v, iterations=1)
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img_bin_h = apply_motion_blur(img_bin_h, 100, 0)
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img_bin_v = apply_motion_blur(img_bin_v, 100, 90)
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img_bin_final = img_bin_h | img_bin_v
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show_mpl(img_bin_final)
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# th 130
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# th1, img_bin_final = cv2.threshold(img_bin_final, 150, 255, cv2.THRESH_BINARY)
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# show_mpl(img_bin_final)
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th1, img_bin_final = cv2.threshold(img_bin_final, 110, 255, cv2.THRESH_BINARY)
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img_bin_final = cv2.dilate(img_bin_final, np.ones((1, 1), np.uint8), iterations=1)
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show_mpl(img_bin_final)
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img_bin_final = disconnect_non_existing_cells(img_bin_final, bounding_rects)
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show_mpl(img_bin_final)
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return img_bin_final
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# def isolate_vertical_and_horizontal_components(img_bin):
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# line_min_width = 30
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# kernel_h = np.ones((1, line_min_width), np.uint8)
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# kernel_v = np.ones((line_min_width, 1), np.uint8)
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#
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# img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
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# img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
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# show_mpl(img_bin_h | img_bin_v)
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#
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# kernel_h = np.ones((1, 30), np.uint8)
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# kernel_v = np.ones((30, 1), np.uint8)
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# img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=1)
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# img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=1)
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# show_mpl(img_bin_h | img_bin_v)
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#
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# img_bin_h = apply_motion_blur(img_bin_h, 100, 0)
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# img_bin_v = apply_motion_blur(img_bin_v, 100, 90)
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#
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# img_bin_final = img_bin_h | img_bin_v
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# show_mpl(img_bin_final)
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# # th 130
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# th1, img_bin_final = cv2.threshold(img_bin_final, 125, 255, cv2.THRESH_BINARY)
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# show_mpl(img_bin_final)
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#
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# return img_bin_final
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def disconnect_non_existing_cells(img_bin, bounding_rects):
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for rect in bounding_rects:
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x, y, w, h = rect
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img_bin = cv2.rectangle(img_bin, (x, y), (x + w, y + h), (0, 0, 0), 5)
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return img_bin
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# FIXME: does not work yet
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@ -163,18 +103,28 @@ def apply_motion_blur(image, size, angle):
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return cv2.filter2D(image, -1, k)
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def find_table_layout_boxes(image: np.array):
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layout_boxes = parse_layout(image)
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table_boxes = []
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for box in layout_boxes:
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(x, y, w, h) = box
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if w * h >= 300000:
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table_boxes.append(box)
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return table_boxes
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def parse_table(image: np.array):
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def is_large_enough(stat):
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x1, y1, w, h, area = stat
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return area > 500 and w > 35 and h > 15
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return area > 2000 and w > 35 and h > 25
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gray_scale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# blur_gray_scale = cv2.GaussianBlur(gray_scale, (5, 5), 1, borderType=cv2.BORDER_REPLICATE)
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th1, img_bin = cv2.threshold(gray_scale, 195, 255, cv2.THRESH_BINARY)
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img_bin = ~img_bin
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show_mpl(img_bin)
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img_bin = isolate_vertical_and_horizontal_components(img_bin)
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table_layout_boxes = find_table_layout_boxes(image)
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img_bin = isolate_vertical_and_horizontal_components(img_bin, table_layout_boxes)
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img_bin_final = add_external_contours(img_bin, img_bin)
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_, _, stats, _ = cv2.connectedComponentsWithStats(~img_bin_final, connectivity=8, ltype=cv2.CV_32S)
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@ -30,7 +30,7 @@ def remove_included(rectangles):
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return rectangles
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#tolerance was set too low (1) most lines are 2px wide
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def adjacent1d(n, m, tolerance=2):
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def adjacent1d(n, m, tolerance=4):
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return abs(n - m) <= tolerance
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