def temp_filter_method_adaptive_thresholding(imageFile): img = data.imread(imageFile, as_grey=True) global_thresh = threshold_yen(img) # True False binary matrix represent color value of the img using global thresholding binary_global = img > global_thresh block_size = 40 # True False binary matrix represent color value of the img using adaptive thresholding binary_adaptive = threshold_adaptive(img, block_size, offset=0) # 0 1 binary matrix img_bin_global = clear_border(img_as_uint(binary_global)) # 0 1 binary matrix img_bin_adaptive = clear_border(img_as_uint(binary_adaptive)) bin_pos_mat = ocr.binary_matrix_to_position(binary_adaptive) np.savetxt("test.txt",bin_pos_mat) # %.5f specifies 5 decimal round
global_thresh = threshold_yen(img) # True False binary matrix represent color value of the img using global thresholding binary_global = img > global_thresh block_size = 40 # True False binary matrix represent color value of the img using adaptive thresholding binary_adaptive = threshold_adaptive(img, block_size, offset=10) # 0 1 binary matrix img_bin_global = clear_border(img_as_uint(binary_global)) # 0 1 binary matrix img_bin_adaptive = clear_border(img_as_uint(binary_adaptive)) bin_pos_mat = ocr.binary_matrix_to_position(binary_adaptive) np.savetxt("test.txt", bin_pos_mat) # %.5f specifies 5 decimal round fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax0, ax1, ax2 = axes figure(1) subplot(311) imshow(image) subplot(312) scatter(bin_pos_mat[:, 1], bin_pos_mat[:, 0]) # imshow(np.flipud(clustered)) subplot(313) imshow(binary_adaptive) show()
# True False binary matrix represent color value of the img using global thresholding binary_global = img > global_thresh block_size = 40 # True False binary matrix represent color value of the img using adaptive thresholding binary_adaptive = threshold_adaptive(img, block_size, offset=10) # 0 1 binary matrix img_bin_global = clear_border(img_as_uint(binary_global)) # 0 1 binary matrix img_bin_adaptive = clear_border(img_as_uint(binary_adaptive)) bin_pos_mat = ocr.binary_matrix_to_position(binary_adaptive) np.savetxt("test.txt",bin_pos_mat) # %.5f specifies 5 decimal round fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax0, ax1, ax2 = axes figure(1) subplot(311) imshow(image) subplot(312) scatter(bin_pos_mat[:,1], bin_pos_mat[:,0]) # imshow(np.flipud(clustered)) subplot(313) imshow(binary_adaptive) show()
print bin_pos_mat # np.savetxt("test.txt",bin_pos_mat) ======= from skimage.data import camera from skimage import data, img_as_uint, img_as_float from skimage.filters import roberts, sobel, scharr, prewitt imageFile = '../pics/beach2.png' image = data.imread(imageFile, as_grey=True) edge_roberts = roberts(image) edge_sobel = sobel(image) bin_pos_mat = ocr.binary_matrix_to_position(edge_sobel) np.savetxt("test.txt",bin_pos_mat) >>>>>>> 57878957f56a122b604e36c48abb4757808fca1d #============================================================ #============================================================ # comment out either temp_filter_method_adaptive_thresholding() # or temp_filter_method_kmeans_color() to try out # the two different filtering methods #============================================================ #============================================================