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get_marginals(): simplify, improve…
- rename `thickness_along_y_percent` → `max_textline_thickness_percent` - rename `marginlas_should_be_main_text` → `main_text_should_be_marginals` - constrain `find_peaks()` by prominence and distance - simplify (a lot) - add comments for possible improvements and for plotting
This commit is contained in:
Robert Sachunsky
parent
bb092364af
commit
70bf461c30
1 changed files with 127 additions and 133 deletions
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@ -7,165 +7,159 @@ from .resize import resize_image
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from .rotate import rotate_image
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def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
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mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
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mask_marginals=mask_marginals.astype(np.uint8)
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# rs: text_with_lines should be called text_mask_d
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# rs: text_regions should be called early_layout (contains other classes, too)
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# rs: text_with_lines is already deskewed, while text_regions is not...
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mask_marginals = np.zeros_like(text_with_lines)
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height, width = mask_marginals.shape
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text_with_lines=text_with_lines.astype(np.uint8)
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##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
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text_with_lines_eroded = cv2.erode(text_with_lines,kernel,iterations=5)
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if text_with_lines.shape[0]<=1500:
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if height <= 1500:
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pass
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elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
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text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
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elif 1500 < height <= 1800:
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# rs: why not / 1.5???
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text_with_lines = resize_image(text_with_lines, int(height * 1.5), width)
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text_with_lines = cv2.erode(text_with_lines, kernel, iterations=5)
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text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
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# rs: and back to original size
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text_with_lines = resize_image(text_with_lines, height, width)
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else:
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text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
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# rs: why not / 1.8???
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text_with_lines = resize_image(text_with_lines, int(height * 1.8), width)
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text_with_lines = cv2.erode(text_with_lines, kernel, iterations=7)
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text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
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# rs: and back to original size
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text_with_lines = resize_image(text_with_lines, height, width)
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kernel_hor = np.ones((1, 5), dtype=np.uint8)
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text_with_lines = cv2.erode(text_with_lines, kernel_hor, iterations=6)
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text_with_lines_y = text_with_lines.sum(axis=0)
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text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
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thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
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max_textline_thickness_percent = 100. * text_with_lines_y_eroded.max() / height
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#print(thickness_along_y_percent,'thickness_along_y_percent')
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if thickness_along_y_percent<30:
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# rs: min_textline_thickness seems to be calibrated for some fixed resolution,
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# but text_with_lines varies in size!
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if max_textline_thickness_percent < 30:
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min_textline_thickness = 8
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elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
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elif max_textline_thickness_percent < 50:
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min_textline_thickness = 20
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else:
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min_textline_thickness = 45
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# min_textline_thickness = max_textline_thickness_percent / 100. * height / 20.
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# plt.figure()
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# ax1 = plt.subplot(2, 1, 1, title="text_with_lines_eroded")
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# ax1.imshow(text_with_lines_eroded, aspect='auto')
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# ax2 = plt.subplot(2, 1, 2, title="text_with_lines_y_eroded", sharex=ax1)
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# ax2.plot(list(range(width)), text_with_lines_y_eroded)
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# ax2.hlines(int(0.14 * height), 0, width,
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# label='max_textline_thickness=14%', colors='r')
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# ax2.hlines([min_textline_thickness], 0, width,
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# label='min_textline_thickness', colors='g')
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# ax2.scatter([np.argmax(text_with_lines_y_eroded)],
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# [text_with_lines_y_eroded.max()], color='r',
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# label='max = %d%%' % max_textline_thickness_percent)
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# plt.legend()
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# plt.show()
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if thickness_along_y_percent>=14:
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text_with_lines_y_rev=-1*text_with_lines_y[:]
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text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
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sigma_gaus=1
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region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
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region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
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region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
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first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
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last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
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last_nonzero=len(region_sum_0)-last_nonzero
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mid_point=(last_nonzero+first_nonzero)/2.
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if max_textline_thickness_percent >= 14:
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text_with_lines_y_rev = np.max(text_with_lines_y) - text_with_lines_y
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region_sum_0 = gaussian_filter1d(text_with_lines_y, 1)
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first_nonzero = region_sum_0.nonzero()[0][0] # outer left
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last_nonzero = region_sum_0.nonzero()[0][-1] # outer right
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mid_point = 0.5 * (last_nonzero + first_nonzero)
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one_third_right = (last_nonzero - mid_point) / 3.0
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one_third_left = (mid_point - first_nonzero) / 3.0
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peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
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peaks=np.array(peaks)
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# rs: constrain the distance at least 2 characters at 12pt, retrieve height and prominence
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peaks, props = find_peaks(text_with_lines_y_rev, height=0, prominence=0, distance=30)
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peaks_orig = np.copy(peaks)
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# rs: also calculate the product of prominence and height (for final selection)
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scores = np.zeros(peaks.max() + 1)
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scores[peaks] = props['prominences'] * props['peak_heights']
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peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)]
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peaks = peaks[region_sum_0[peaks] < min_textline_thickness]
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if num_col == 1:
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peaks_right = peaks[peaks > mid_point]
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peaks_left = peaks[peaks < mid_point]
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if num_col == 2:
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peaks_right=peaks[peaks>(mid_point+one_third_right)]
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peaks_left=peaks[peaks<(mid_point-one_third_left)]
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peaks_right = peaks[peaks > mid_point + one_third_right]
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peaks_left = peaks[peaks < mid_point - one_third_left]
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try:
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point_right=np.min(peaks_right)
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except:
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point_right=last_nonzero
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try:
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point_left=np.max(peaks_left)
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except:
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point_left=first_nonzero
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if point_left == first_nonzero and point_right == last_nonzero:
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point_right = np.min(peaks_right, initial=last_nonzero)
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point_left = np.max(peaks_left, initial=first_nonzero)
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# rs: at least one peak must have been found
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if point_right == last_nonzero and point_left == first_nonzero:
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return text_regions
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if point_right>=mask_marginals.shape[1]:
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point_right=mask_marginals.shape[1]-1
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try:
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# rs: should be called mask_main (i.e. inverted semantics here)
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mask_marginals[:, point_left: point_right] = 1
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except:
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mask_marginals[:,:]=1
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# plt.figure()
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# ax1 = plt.subplot(2, 2, 1)
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# ax1.title.set_text('text_with_lines (deskewed text+sep mask)')
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# ax1.imshow(text_with_lines)
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# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='b')
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# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='b')
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# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='g')
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# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='g')
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# ax1.vlines([point_left], 0, height, label='point_left', colors='r')
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# ax1.vlines([point_right], 0, height, label='point_right', colors='r')
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# ax2 = plt.subplot(2, 2, 2, title='mask_marginals (deskewed marginal mask)', sharey=ax1)
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# ax2.imshow(mask_marginals)
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# ax3 = plt.subplot(2, 2, 3, title='text_with_lines_y (projection for minima)', sharex=ax1)
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# ax3.plot(list(range(width)), text_with_lines_y)
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# ax3.set_aspect('auto')
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# ax4 = plt.subplot(2, 2, 4, title='text_regions (undeskewed labels)')
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# ax4.imshow(text_regions)
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# plt.legend()
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# plt.show()
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# rs: rotate back (into undeskewed/original shape as text_regions input):
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mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew)
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mask_marginals_rotated_y = mask_marginals_rotated.sum(axis=0)
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mask_marginals_rotated_y_nz = np.flatnonzero(mask_marginals_rotated_y)
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min_point_of_left_marginal = max(0, np.min(mask_marginals_rotated_y_nz) - 16)
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max_point_of_right_marginal = min(width - 1, np.max(mask_marginals_rotated_y_nz) + 16)
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mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
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mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
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index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
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index_x_interest=index_x[mask_marginals_rotated_sum==1]
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min_point_of_left_marginal=np.min(index_x_interest)-16
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max_point_of_right_marginal=np.max(index_x_interest)+16
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if min_point_of_left_marginal<0:
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min_point_of_left_marginal=0
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if max_point_of_right_marginal>=text_regions.shape[1]:
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max_point_of_right_marginal=text_regions.shape[1]-1
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text_regions_org = np.copy(text_regions)
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text_regions[text_regions[:,:]==1]=4
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pixel_img=4
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min_area_text = 0.00001
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# rs: why not extract from mask_marginals_rotated???
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# rs: why not largest area instead of first?
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polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals, 1, min_area_text)[0]
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polygons_of_marginals = return_contours_of_interested_region(text_regions, 1, min_area_text)
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polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals,1,min_area_text)
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polygon_mask_marginals_rotated = polygon_mask_marginals_rotated[0]
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polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
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cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals)
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text_regions[(text_regions[:,:]==4)]=1
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marginlas_should_be_main_text=[]
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(cx_text_only,
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cy_text_only,
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x_min_text_only,
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x_max_text_only,
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y_min_text_only,
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y_max_text_only,
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y_cor_x_min_main) = find_new_features_of_contours(polygons_of_marginals)
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main_text_should_be_marginals = []
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x_min_marginals_left=[]
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x_min_marginals_right=[]
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for i in range(len(cx_text_only)):
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results = cv2.pointPolygonTest(polygon_mask_marginals_rotated, (cx_text_only[i], cy_text_only[i]), False)
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for i, polygon in enumerate(polygons_of_marginals):
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if -1 == cv2.pointPolygonTest(polygon_mask_marginals_rotated,
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(cx_text_only[i],
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cy_text_only[i]),
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False):
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main_text_should_be_marginals.append(polygon)
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if results == -1:
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marginlas_should_be_main_text.append(polygons_of_marginals[i])
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text_regions_org=cv2.fillPoly(text_regions_org, pts =marginlas_should_be_main_text, color=(4,4))
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text_regions = np.copy(text_regions_org)
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###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
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###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
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#plt.plot(region_sum_0)
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#plt.plot(peaks,region_sum_0[peaks],'*')
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text_regions = cv2.fillPoly(text_regions, pts=main_text_should_be_marginals, color=4)
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# plt.figure()
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# ax1 = plt.subplot(2, 2, 1, title='mask_marginals (deskewed marginal mask)')
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# plt.imshow(mask_marginals)
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# ax2 = plt.subplot(2, 2, 2, title='mask_marginals_rotated (undeskewed marginal mask)')
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# plt.imshow(mask_marginals_rotated)
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# ax4 = plt.subplot(2, 2, 4, title='text_regions (undeskewed labels split)')
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# plt.imshow(text_regions)
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# plt.show()
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#plt.imshow(text_regions)
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#plt.show()
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