def test_matrix(self): original = np.array([[(255, 255, 255), (48, 113, 219), (0, 0, 0)], [(0, 0, 0), (48, 113, 219), (255, 255, 255)], [(48, 113, 219), (0, 0, 0), (0, 0, 0)], [(255, 255, 255), (48, 113, 219), (255, 255, 255)]]) expected = np.array([[(255, 128, 128), (137, 186, 78), (0, 128, 128)], [(0, 128, 128), (137, 186, 78), (255, 128, 128)], [(137, 186, 78), (0, 128, 128), (0, 128, 128)], [(255, 128, 128), (137, 186, 78), (255, 128, 128)]]) actual = imagetools.bgr_to_ycrcb(original) np.testing.assert_array_equal( expected, actual, "The original pixel- {} converted to {} and not to {} that expected" .format(original, actual, expected))
dest=os.path.join(src_dir, mode + '_channel_c.png')) print('Downsampling ' + mode) img_channel_b = encode.upsample(encode.downsample(img_channel_b)) img_channel_c = encode.upsample(encode.downsample(img_channel_c)) imagetools.save_matrix(encode.concatenate_three_colors( img_channel_a, img_channel_b, img_channel_c), mode=mode, dest=os.path.join(src_dir, mode + '_downsapling.png')) split_and_downsample(img, 'BGR') print("bgr_to_ycrcb") img = imagetools.bgr_to_ycrcb(img) split_and_downsample(img, 'YCrCb') def local_dct(matrix, dst, size=8): y, cr, cb = encode.split_to_three_colors(matrix) y_shape = encode.shape_for_contacting(y.shape, size) cb_shape = encode.shape_for_contacting(cb.shape, size) cr_shape = encode.shape_for_contacting(cr.shape, size) print("Split and padding to submatrices") y = [ matrix for matrix in encode.split_matrix_into_sub_matrices(y, size) ] cr = [
def compress_image(src_path, dest_path, entropy=False, size=8) -> bool: print("Reading file") bitmap = imagetools.get_bitmap_from_bmp(src_path) if entropy: print("Bitmap entropy: " + str(ent.entropy(bitmap))) print("Crop image") bitmap = crop_bitmap(bitmap) print("Converting to YCrCb") bitmap = imagetools.bgr_to_ycrcb(bitmap) print("Separating bitmap to Y, Cb, Cr matrices") y, cb, cr = split_to_three_colors(bitmap) print("Downsampling") cr = downsample(cr) cb = downsample(cb) y_shape = shape_for_contacting(y.shape, size) cr_shape = shape_for_contacting(cr.shape, size) cb_shape = shape_for_contacting(cb.shape, size) print("Splitting to {0}x{0} sub-matrices".format(size)) y = split_matrix_into_sub_matrices(y, size) cr = split_matrix_into_sub_matrices(cr, size) cb = split_matrix_into_sub_matrices(cb, size) print("dct") y = [dct.dct(sub_matrix) for sub_matrix in y] cr = [dct.dct(sub_matrix) for sub_matrix in cr] cb = [dct.dct(sub_matrix) for sub_matrix in cb] print("Quantization") y = [dct.quantization(submatrix) for submatrix in y] cr = [dct.quantization(submatrix) for submatrix in cr] cb = [dct.quantization(submatrix) for submatrix in cb] if entropy: print("Compressed entropy: " + str( ent.entropy(np.dstack([np.dstack( y), np.dstack(cr), np.dstack(cb)])))) print("UnQuantization") y = [dct.un_quantization(submatrix) for submatrix in y] cr = [dct.un_quantization(submatrix) for submatrix in cr] cb = [dct.un_quantization(submatrix) for submatrix in cb] print("Invert dct") y = [dct.inverse_dct(matrix) for matrix in y] cr = [dct.inverse_dct(matrix) for matrix in cr] cb = [dct.inverse_dct(matrix) for matrix in cb] print("Concatenate") y = concatenate_sub_matrices_to_big_matrix(y, y_shape) cr = concatenate_sub_matrices_to_big_matrix(cr, cb_shape) cb = concatenate_sub_matrices_to_big_matrix(cb, cr_shape) print("upsample") cr = upsample(cr) cb = upsample(cb) print("concatenate") concatenate_three_colors(y, cr, cb, bitmap) # print("ycrcb_to_bgr") # bitmap = imagetools.ycrcb_to_bgr(bitmap) print("save_matrix") imagetools.save_matrix(bitmap, mode='YCrCb', dest=dest_path + '.png')
def compress_image(src_path, dest_path, entropy=False, size=8): # pragma: no cover print("Reading file") bitmap = imagetools.get_bitmap_from_bmp(src_path) if entropy: print("Bitmap entropy: " + str(ent.entropy(bitmap))) print("Crop image") ycrcb_crop = crop_bitmap(bitmap) print("Converting to YCrCb") ycrcb_bitmap = imagetools.bgr_to_ycrcb(ycrcb_crop) print("Separating bitmap to Y, Cb, Cr matrices") y, cb, cr = split_to_three_colors(ycrcb_bitmap) print("Downsampling") cb_downsample = downsample(cb) cr_downsample = downsample(cr) y_shape = shape_for_contacting(y.shape, size) cb_shape = shape_for_contacting(cb_downsample.shape, size) cr_shape = shape_for_contacting(cr_downsample.shape, size) print("Splitting to 8x8 sub-matrices") y_split = split_matrix_into_sub_matrices(y) cb_split = split_matrix_into_sub_matrices(cb_downsample) cr_split = split_matrix_into_sub_matrices(cr_downsample) print("DCT") y_dct = [dct.DCT(sub_matrix) for sub_matrix in y_split] cb_dct = [dct.DCT(sub_matrix) for sub_matrix in cb_split] cr_dct = [dct.DCT(sub_matrix) for sub_matrix in cr_split] print("Quantization") y_quantization = [dct.quantization(submatrix) for submatrix in y_dct] cb_quantization = [dct.quantization(submatrix) for submatrix in cb_dct] cr_quantization = [dct.quantization(submatrix) for submatrix in cr_dct] if entropy: print("Compressed entropy: " + str( ent.entropy( np.array([y_quantization, cb_quantization, cr_quantization])))) print("UnQuantization") y_un_quantization = [ dct.un_quantization(submatrix) for submatrix in y_quantization ] cb_un_quantization = [ dct.un_quantization(submatrix) for submatrix in cb_quantization ] cr_un_quantization = [ dct.un_quantization(submatrix) for submatrix in cr_quantization ] print("Invert DCT") y_invert_dct = [dct.inverse_DCT(matrix) for matrix in y_un_quantization] cb_invert_dct = [dct.inverse_DCT(matrix) for matrix in cb_un_quantization] cr_invert_dct = [dct.inverse_DCT(matrix) for matrix in cr_un_quantization] print("Concatenate") y_big = concatenate_sub_matrices_to_big_matrix(y_invert_dct, y_shape) cb_big = concatenate_sub_matrices_to_big_matrix(cb_invert_dct, cb_shape) cr_big = concatenate_sub_matrices_to_big_matrix(cr_invert_dct, cr_shape) print("upsample") cb_upsample = upsample(cb_big) cr_upsample = upsample(cr_big) new_image = concatenate_three_colors(y_big, cb_upsample, cr_upsample) imagetools.save_matrix(new_image, mode='YCrCb', dest=dest_path + '.png')
def test_vs_cv2(self): im = cv2.imread(os.path.join(src, "colored.bmp")) expected = cv2.cvtColor(im, cv2.COLOR_BGR2YCrCb) actual = imagetools.bgr_to_ycrcb(im) different = np.count_nonzero(actual - expected) / np.prod(im.shape) self.assertLessEqual(different, 1 / 100)