def hough_cornering(image, orig, crop_box): # added original for plotting
original = image.copy(
) # necessary? Need to walk through these methods and see what they change
downsized, ratio = reshape.standard_resize(image, new_width=100.0)
edged = edging.page_edging(downsized, thresh1=0, thresh2=160) # good
lines = standard_hough_lines(edged)
corners = np.zeros(shape=(4, 2))
if len(lines) < 70:
corners = all_corners(lines) # TRYING THIS 4/18
else:
print("TOO MANY HOUGH LINES AT " + str(len(lines)))
success = False
filtered_corners = np.zeros(shape=(4, 2))
upsized_filtered_corners = np.zeros(shape=(4, 2))
if len(corners) >= 4:
# filtered_corners, success = corner_filtration(corner_array) # old method, didn't work.
image_area = downsized.shape[0] * downsized.shape[1]
filtered_corners, success = corner_filter(corners,
image_area=image_area)
upsized_filtered_corners = filtered_corners * ratio
upsized_filtered_corners = np.int0(upsized_filtered_corners)
xmin, xmax, ymin, ymax = boxing.max_points(crop_box)
upsized_filtered_corners[:, 0] += xmin
upsized_filtered_corners[:, 1] += ymin
upsized_filtered_corners = tuple(map(tuple, upsized_filtered_corners))
'''This works when 4 corners are passed in. Need to figure out how to filter down to 4 corners or skip it if no corners found.'''
return upsized_filtered_corners, success
def hough_detection(image):
original = image.copy(
) # necessary? Need to walk through these methods and see what they change
downsized, ratio = reshape.standard_resize(image, new_width=100.0)
edged = edging.page_edging(downsized, thresh1=0, thresh2=160) # good
lines = standard_hough_lines(edged)
corners = set()
if len(lines) < 70:
corners = all_corners(lines) # TRYING THIS 4/18
else:
print("TOO MANY HOUGH LINES AT " + str(len(lines)))
success = False
if len(corners) >= 4:
downsized_area = downsized.shape[0] * downsized.shape[1]
filtered_corners, success = corner_filter(corners,
image_area=downsized_area)
upsized_filtered_corners = filtered_corners * ratio
upsized_filtered_corners = np.int0(upsized_filtered_corners)
# xmin, xmax, ymin, ymax = boxing.max_points(crop_box)
# upsized_filtered_corners[:, 0] += xmin
# upsized_filtered_corners[:, 1] += ymin
# upsized_filtered_corners = tuple(map(tuple, upsized_filtered_corners))
return upsized_filtered_corners, success
def hough_corners(image, orig, crop_box): # added original for plotting
original = image.copy(
) # necessary? Need to walk through these methods and see what they change
downsized, ratio = reshape.standard_resize(image, new_width=100.0)
edged = edging.page_edging(downsized, thresh1=0, thresh2=160) # good
lines = standard_hough_lines(edged)
# lined = draw_lines(downsized.copy(), lines)
corners = set()
if len(lines) < 70:
corners = all_corners(lines) # TRYING THIS 4/18
else:
print("TOO MANY HOUGH LINES AT " + str(len(lines)))
success = False
if len(corners) >= 4:
image_area = downsized.shape[0] * downsized.shape[1]
filtered_corners, success = corner_filter(corners,
image_area=image_area)
# upsized_corners = corners * ratio
upsized_filtered_corners = filtered_corners * ratio
# upsized_corners = np.int0(upsized_corners)
upsized_filtered_corners = np.int0(upsized_filtered_corners)
xmin, xmax, ymin, ymax = boxing.max_points(crop_box)
# upsized_corners[:, 0] += xmin
# upsized_corners[:, 1] += ymin
upsized_filtered_corners[:, 0] += xmin
upsized_filtered_corners[:, 1] += ymin
# upsized_corners = tuple(map(tuple, upsized_corners))
upsized_filtered_corners = tuple(map(tuple, upsized_filtered_corners))
# cornered = draw_corners(orig, upsized_corners)
# if success:
# cornered = draw_corners(cornered, upsized_filtered_corners, color=(255,0,0))
# warped = reshape.perspective_transform(image=orig, points=upsized_filtered_corners)
# threshed = coloring.thresholding(warped)
# functions.plot_images([orig, image, edged, lined, cornered, threshed], titles=['Original', 'Cropped', 'Canny', 'Hough lines', 'Corners', 'Transform'])
'''This works when 4 corners are passed in. Need to figure out how to filter down to 4 corners or skip it if no corners found.'''
return upsized_filtered_corners
def hough_detection(image):
# original = image.copy() # necessary? Need to walk through these methods and see what they change
downsized, ratio = reshape.standard_resize(image, new_width=100.0)
edged = edging.page_edging(downsized, thresh1=0, thresh2=160) # good
lines = standard_hough_lines(edged)
# corners = np.zeros(shape=(4,2))
# if len(lines) < 70:
corners = all_corners(lines) # TRYING THIS 4/18
success = False
# if len(corners) >= 4 and np.linalg.norm(corners) > 0:
if len(corners) >= 4:
downsized_area = downsized.shape[0] * downsized.shape[1]
filtered_corners, success = corner_filter(corners,
image_area=downsized_area)
corners = np.array(list(filtered_corners))
print("original corners: " + str(corners))
corners = corners * ratio
print("final corners: " + str(corners))
return corners, success
def hough_cornering(image, orig): # added original for plotting
original = image.copy(
) # necessary? Need to walk through these methods and see what they change
downsized, ratio = reshape.standard_resize(image, new_width=100.0)
# edged = edging.orig_page_edging(downsized) # bad
edged = edging.page_edging(downsized, thresh1=0, thresh2=160) # good
# edged = edging.auto_edging(downsized, sigma=0.5) # not great
lines = standard_hough_lines(edged)
lined = draw_lines(downsized.copy(), lines)
# functions.plot_image(edged)
corners = set()
if len(lines) < 70:
# corners = hough_corners(lines) # COMMENTED OUT 4/18
print("FEWER than 30 lines...finding CORNERS")
corners = all_corners(lines) # TRYING THIS 4/18
print("Here are the corners in hough cornering")
print(corners)
else:
print("TOO MANY HOUGH LINES AT " + str(len(lines)))
success = False
if len(corners) >= 4:
# filtered_corners, success = corner_filtration(corner_array) # old method, didn't work.
image_area = downsized.shape[0] * downsized.shape[1]
filtered_corners, success = corner_filter(corners,
image_area=image_area)
print("CORNERS after corner filtration")
print(filtered_corners)
'''Compute bounding box size from corners. If too small, success = False. O
OR add a check in the filtration method that ensures the range of all axes is reasonable
Reason: came across a case where there were 4 points really close together that the logic interpreted as valid
because it is using the global range defined in the method from the corners there '''
upsized_corners = corners * ratio
upsized_filtered_corners = filtered_corners * ratio
upsized_corners = np.int0(upsized_corners)
upsized_filtered_corners = np.int0(upsized_filtered_corners)
upsized_corners = tuple(map(tuple, upsized_corners))
upsized_filtered_corners = tuple(map(tuple, upsized_filtered_corners))
# cornered = draw_corners(downsized, corners)
cornered = draw_corners(original, upsized_corners)
if success:
# cornered = draw_corners(cornered, filtered_corners, color=(255,0,0))
cornered = draw_corners(cornered,
upsized_filtered_corners,
color=(255, 0, 0))
warped = reshape.perspective_transform(image=original,
points=filtered_corners,
ratio=ratio)
threshed = coloring.thresholding(warped)
# functions.plot_images([image, lined, cornered, threshed], titles=['Original', 'Hough lines', 'Corners', 'Transform'])
functions.plot_images([orig, image, edged, lined, cornered, threshed],
titles=[
'Original', 'Cropped', 'Canny',
'Hough lines', 'Corners', 'Transform'
])
# functions.plot_images([original, edged, lined, cornered, threshed], titles=['Original', 'edged', 'Hough lines', 'Corners', 'Transform'])
else:
functions.plot_images([image, edged, lined, cornered],
titles=['image', 'edged', 'lined', 'cornered'])
def hough_cornering(image, orig, crop_box): # added original for plotting
original = image.copy(
) # necessary? Need to walk through these methods and see what they change
downsized, ratio = reshape.standard_resize(image, new_width=100.0)
# edged = edging.orig_page_edging(downsized) # bad
edged = edging.page_edging(downsized, thresh1=0, thresh2=160) # good
# edged = edging.auto_edging(downsized, sigma=0.5) # not great
lines = standard_hough_lines(edged)
lined = draw_lines(downsized.copy(), lines)
# functions.plot_image(edged)
# corners = set()
corners = np.zeros(shape=(4, 2))
if len(lines) < 70:
# corners = hough_corners(lines) # COMMENTED OUT 4/18
# print("FEWER than 30 lines...finding CORNERS")
corners = all_corners(lines) # TRYING THIS 4/18
# print("Here are the corners in hough cornering")
# print(corners)
else:
print("TOO MANY HOUGH LINES AT " + str(len(lines)))
success = False
filtered_corners = np.zeros(shape=(4, 2))
upsized_filtered_corners = np.zeros(shape=(4, 2))
if len(corners) >= 4:
# filtered_corners, success = corner_filtration(corner_array) # old method, didn't work.
image_area = downsized.shape[0] * downsized.shape[1]
filtered_corners, success = corner_filter(corners,
image_area=image_area)
# print("CORNERS after corner filtration")
# print(filtered_corners)
'''Compute bounding box size from corners. If too small, success = False. O
OR add a check in the filtration method that ensures the range of all axes is reasonable
Reason: came across a case where there were 4 points really close together that the logic interpreted as valid
because it is using the global range defined in the method from the corners there '''
# print("corners before upsizeing")
# print(corners)
# print(type(corners))
upsized_corners = corners * ratio
upsized_filtered_corners = filtered_corners * ratio
upsized_corners = np.int0(upsized_corners)
upsized_filtered_corners = np.int0(upsized_filtered_corners)
xmin, xmax, ymin, ymax = boxing.max_points(crop_box)
# print("UPSIZED CORNERS")
# print(upsized_corners)
# upsized_corners[:, 0] += xmin
# upsized_corners[:, 1] += ymin
# xmin, xmax, ymin, ymax = boxing.max_points(upsized_filtered_corners)
# upsized_filtered_corners[:, 0] += xmin
# upsized_filtered_corners[:, 1] += ymin
upsized_filtered_corners[:,
0] = np.add(upsized_filtered_corners[:, 0],
xmin,
out=upsized_filtered_corners[:,
0],
casting="unsafe")
upsized_filtered_corners[:,
1] = np.add(upsized_filtered_corners[:, 1],
ymin,
out=upsized_filtered_corners[:,
1],
casting="unsafe")
# upsized_corners = tuple(map(tuple, upsized_corners))
upsized_filtered_corners = tuple(map(tuple, upsized_filtered_corners))
# cornered = draw_corners(downsized, corners)
# cornered = draw_corners(original, upsized_corners)
# cornered = draw_corners(orig, upsized_corners) # was here
# if success:
# cornered = draw_corners(cornered, filtered_corners, color=(255,0,0))
# cornered = draw_corners(cornered, upsized_filtered_corners, color=(255,0,0)) # was here
# warped = reshape.perspective_transform(image=original, points=filtered_corners, ratio=ratio)
# warped = reshape.perspective_transform(image=orig, points=upsized_filtered_corners)
# threshed = coloring.thresholding(warped)
# functions.plot_images([image, lined, cornered, threshed], titles=['Original', 'Hough lines', 'Corners', 'Transform'])
# functions.plot_images([orig, image, edged, lined, cornered, threshed], titles=['Original', 'Cropped', 'Canny', 'Hough lines', 'Corners', 'Transform']) # was here
# functions.plot_images([original, edged, lined, cornered, threshed], titles=['Original', 'edged', 'Hough lines', 'Corners', 'Transform'])
# else:
# functions.plot_images([image, edged, lined, cornered], titles=['image', 'edged', 'lined', 'cornered']) # was here
# print('hello dolly')
'''This works when 4 corners are passed in. Need to figure out how to filter down to 4 corners or skip it if no corners found.'''
return upsized_filtered_corners, success