mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-31 01:34:17 +01:00
1265 lines
54 KiB
Python
1265 lines
54 KiB
Python
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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()
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x_cont = contour_text_interest[:, 0, 0]
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y_cont = contour_text_interest[:, 0, 1]
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x_cont = x_cont - np.min(x_cont)
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y_cont = y_cont - np.min(y_cont)
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x_min_cont = 0
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x_max_cont = img_patch.shape[1]
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y_min_cont = 0
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y_max_cont = img_patch.shape[0]
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xv = np.linspace(x_min_cont, x_max_cont, 1000)
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textline_patch_sum_along_width = img_patch.sum(axis=1)
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first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
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y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
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y_padded = np.zeros(len(y) + 40)
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y_padded[20 : len(y) + 20] = y
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x = np.array(range(len(y)))
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peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
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if 1 > 0:
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try:
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y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
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y_padded_up_to_down_e = -y_padded + np.max(y_padded)
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y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
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y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
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y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
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peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
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peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
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neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
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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]
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diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
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arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
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arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
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peaks_new = peaks_e[:]
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peaks_neg_new = peaks_neg_e[:]
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clusters_to_be_deleted = []
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if len(arg_diff_cluster) > 0:
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
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for i in range(len(arg_diff_cluster) - 1):
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
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if len(clusters_to_be_deleted) > 0:
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peaks_new_extra = []
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for m in range(len(clusters_to_be_deleted)):
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min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
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max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
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peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
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for m1 in range(len(clusters_to_be_deleted[m])):
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peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
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peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
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peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
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peaks_new_tot = []
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for i1 in peaks_new:
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peaks_new_tot.append(i1)
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for i1 in peaks_new_extra:
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peaks_new_tot.append(i1)
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peaks_new_tot = np.sort(peaks_new_tot)
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else:
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peaks_new_tot = peaks_e[:]
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textline_con, hierachy = return_contours_of_image(img_patch)
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textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
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y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
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sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
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# print(sigma_gaus,'sigma_gaus')
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except:
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sigma_gaus = 12
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if sigma_gaus < 3:
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sigma_gaus = 3
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# print(sigma_gaus,'sigma')
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y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
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y_padded_up_to_down = -y_padded + np.max(y_padded)
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y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
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y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
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y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
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peaks, _ = find_peaks(y_padded_smoothed, height=0)
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peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
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try:
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neg_peaks_max = np.max(y_padded_smoothed[peaks])
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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]
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diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
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arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
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arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
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except:
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arg_neg_must_be_deleted = []
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arg_diff_cluster = []
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try:
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peaks_new = peaks[:]
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peaks_neg_new = peaks_neg[:]
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clusters_to_be_deleted = []
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if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
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for i in range(len(arg_diff_cluster) - 1):
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
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elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
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clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
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if len(arg_neg_must_be_deleted) == 1:
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clusters_to_be_deleted.append(arg_neg_must_be_deleted)
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if len(clusters_to_be_deleted) > 0:
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peaks_new_extra = []
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for m in range(len(clusters_to_be_deleted)):
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min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
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max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
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peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
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for m1 in range(len(clusters_to_be_deleted[m])):
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peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
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peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
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peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
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peaks_new_tot = []
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for i1 in peaks_new:
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peaks_new_tot.append(i1)
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for i1 in peaks_new_extra:
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peaks_new_tot.append(i1)
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peaks_new_tot = np.sort(peaks_new_tot)
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##plt.plot(y_padded_up_to_down_padded)
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##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
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##plt.show()
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##plt.plot(y_padded_up_to_down_padded)
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##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
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##plt.show()
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##plt.plot(y_padded_smoothed)
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##plt.plot(peaks,y_padded_smoothed[peaks],'*')
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##plt.show()
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##plt.plot(y_padded_smoothed)
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##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
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##plt.show()
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peaks = peaks_new_tot[:]
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peaks_neg = peaks_neg_new[:]
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else:
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peaks_new_tot = peaks[:]
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peaks = peaks_new_tot[:]
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peaks_neg = peaks_neg_new[:]
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except:
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pass
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mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
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std_value_of_peaks = np.std(y_padded_smoothed[peaks])
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peaks_values = y_padded_smoothed[peaks]
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peaks_neg = peaks_neg - 20 - 20
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peaks = peaks - 20
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for jj in range(len(peaks_neg)):
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if peaks_neg[jj] > len(x) - 1:
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peaks_neg[jj] = len(x) - 1
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for jj in range(len(peaks)):
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if peaks[jj] > len(x) - 1:
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peaks[jj] = len(x) - 1
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textline_boxes = []
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textline_boxes_rot = []
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if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
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for jj in range(len(peaks)):
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if jj == (len(peaks) - 1):
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dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
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dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
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if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
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point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
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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)
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else:
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point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
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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)
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point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
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else:
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dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
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dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
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if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
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point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
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point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
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else:
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point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
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point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
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point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
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if point_down_narrow >= img_patch.shape[0]:
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point_down_narrow = img_patch.shape[0] - 2
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distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
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distances = np.array(distances)
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xvinside = xv[distances >= 0]
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if len(xvinside) == 0:
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x_min = x_min_cont
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x_max = x_max_cont
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else:
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x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
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x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
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p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
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p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
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p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
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p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
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x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
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x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
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x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
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x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
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if x_min_rot1 < 0:
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x_min_rot1 = 0
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if x_min_rot4 < 0:
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x_min_rot4 = 0
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if point_up_rot1 < 0:
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point_up_rot1 = 0
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if point_up_rot2 < 0:
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point_up_rot2 = 0
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x_min_rot1 = x_min_rot1 - x_help
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x_max_rot2 = x_max_rot2 - x_help
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x_max_rot3 = x_max_rot3 - x_help
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x_min_rot4 = x_min_rot4 - x_help
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point_up_rot1 = point_up_rot1 - y_help
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point_up_rot2 = point_up_rot2 - y_help
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point_down_rot3 = point_down_rot3 - y_help
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point_down_rot4 = point_down_rot4 - y_help
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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)]]))
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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)]]))
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elif len(peaks) < 1:
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pass
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elif len(peaks) == 1:
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distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) for mj in range(len(xv))]
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distances = np.array(distances)
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xvinside = xv[distances >= 0]
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if len(xvinside) == 0:
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x_min = x_min_cont
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x_max = x_max_cont
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else:
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x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
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x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
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# x_min = x_min_cont
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# x_max = x_max_cont
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y_min = y_min_cont
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y_max = y_max_cont
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p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)])
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p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)])
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p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)])
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p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)])
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x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
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x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
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x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
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x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
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if x_min_rot1 < 0:
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x_min_rot1 = 0
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if x_min_rot4 < 0:
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x_min_rot4 = 0
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if point_up_rot1 < 0:
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point_up_rot1 = 0
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if point_up_rot2 < 0:
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point_up_rot2 = 0
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x_min_rot1 = x_min_rot1 - x_help
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x_max_rot2 = x_max_rot2 - x_help
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x_max_rot3 = x_max_rot3 - x_help
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x_min_rot4 = x_min_rot4 - x_help
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point_up_rot1 = point_up_rot1 - y_help
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point_up_rot2 = point_up_rot2 - y_help
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point_down_rot3 = point_down_rot3 - y_help
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point_down_rot4 = point_down_rot4 - y_help
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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)]]))
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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)]]))
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elif len(peaks) == 2:
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dis_to_next = np.abs(peaks[1] - peaks[0])
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for jj in range(len(peaks)):
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if jj == 0:
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point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
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if point_up < 0:
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point_up = 1
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point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
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elif jj == 1:
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point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
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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 seperate_lines_new_inside_teils(img_path, thetha):
|
|
(h, w) = img_path.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)]])
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_path.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_path.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
mada_n = img_path.sum(axis=1)
|
|
|
|
##plt.plot(mada_n)
|
|
##plt.show()
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = mada_n[:] # [first_nonzero:last_nonzero]
|
|
y_help = np.zeros(len(y) + 40)
|
|
y_help[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
if len(peaks_real) <= 2 and len(peaks_real) > 1:
|
|
sigma_gaus = 10
|
|
else:
|
|
sigma_gaus = 5
|
|
|
|
z = gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
for nn in range(len(peaks_neg)):
|
|
if peaks_neg[nn] > len(z) - 1:
|
|
peaks_neg[nn] = len(z) - 1
|
|
if peaks_neg[nn] < 0:
|
|
peaks_neg[nn] = 0
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg))
|
|
|
|
cut_off = 20
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks_neg)):
|
|
if i == 0:
|
|
forest.append(peaks_neg[i])
|
|
if i < (len(peaks_neg) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg[i + 1])
|
|
if i == (len(peaks_neg) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
diff_peaks_pos = np.abs(np.diff(peaks))
|
|
|
|
cut_off = 20
|
|
peaks_pos_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks)):
|
|
if i == 0:
|
|
forest.append(peaks[i])
|
|
if i < (len(peaks) - 1):
|
|
if diff_peaks_pos[i] <= cut_off:
|
|
forest.append(peaks[i + 1])
|
|
if diff_peaks_pos[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not isNaN(forest[np.argmax(z[forest])]):
|
|
peaks_pos_true.append(forest[np.argmax(z[forest])])
|
|
forest = []
|
|
forest.append(peaks[i + 1])
|
|
if i == (len(peaks) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not isNaN(forest[np.argmax(z[forest])]):
|
|
peaks_pos_true.append(forest[np.argmax(z[forest])])
|
|
|
|
# print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss')
|
|
|
|
if len(peaks_neg_true) > 0:
|
|
peaks_neg_true = np.array(peaks_neg_true)
|
|
"""
|
|
#plt.figure(figsize=(40,40))
|
|
#plt.subplot(1,2,1)
|
|
#plt.title('Textline segmentation von Textregion')
|
|
#plt.imshow(img_path)
|
|
#plt.xlabel('X')
|
|
#plt.ylabel('Y')
|
|
#plt.subplot(1,2,2)
|
|
#plt.title('Dichte entlang X')
|
|
#base = pyplot.gca().transData
|
|
#rot = transforms.Affine2D().rotate_deg(90)
|
|
#plt.plot(zneg,np.array(range(len(zneg))))
|
|
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
|
|
#plt.gca().invert_yaxis()
|
|
|
|
#plt.xlabel('Dichte')
|
|
#plt.ylabel('Y')
|
|
##plt.plot([0,len(y)], [grenze,grenze])
|
|
#plt.show()
|
|
"""
|
|
peaks_neg_true = peaks_neg_true - 20 - 20
|
|
|
|
# print(peaks_neg_true)
|
|
for i in range(len(peaks_neg_true)):
|
|
img_path[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_path[peaks_pos_true[i] - 8 : peaks_pos_true[i] + 8, :] = 1
|
|
else:
|
|
pass
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
# img_path = cv2.erode(img_path,kernel,iterations = 3)
|
|
img_path = cv2.erode(img_path, kernel, iterations=2)
|
|
return img_path
|
|
|
|
def seperate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
pixel = 255
|
|
min_area = 0
|
|
max_area = 1
|
|
|
|
if len(img_patch.shape) == 3:
|
|
cnts_images = (img_patch[:, :, 0] == pixel) * 1
|
|
else:
|
|
cnts_images = (img_patch[:, :] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
|
|
|
|
cont_final = []
|
|
###print(add_boxes_coor_into_textlines,'ikki')
|
|
for i in range(len(contours_imgs)):
|
|
img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
|
|
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))
|
|
|
|
img_contour = img_contour.astype(np.uint8)
|
|
|
|
img_contour = cv2.dilate(img_contour, kernel, iterations=4)
|
|
imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
|
|
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
|
|
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
|
|
##0]
|
|
##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
|
|
##if add_boxes_coor_into_textlines:
|
|
##print(np.shape(contours_text_rot[0]),'sjppo')
|
|
##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0]
|
|
##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1]
|
|
cont_final.append(contours_text_rot[0])
|
|
|
|
##print(cont_final,'nadizzzz')
|
|
return None, cont_final
|
|
|
|
|