def test_imexport_imimport(): shape = (2, 2) image = np.zeros(shape) with expected_warnings(['precision loss']): pil_image = ndarray_to_pil(image) out = pil_to_ndarray(pil_image) assert out.shape == shape
def __call__(self, img): noise_type = random.random() if noise_type < .33: noise_type = 's&p' elif noise_type < .66: noise_type = 'speckle' elif noise_type < 1.: noise_type = 'gaussian' else: return img nd_img = np.array(img) / 255.0 nd_noised = random_noise(np.asarray(nd_img), mode=noise_type, clip=True) img = ndarray_to_pil(nd_noised) return img
def roundtrip_pil_image(self, x): pil_image = ndarray_to_pil(x) y = pil_to_ndarray(pil_image) return y
def test_imexport_imimport(): shape = (2, 2) image = np.zeros(shape) pil_image = ndarray_to_pil(image) out = pil_to_ndarray(pil_image) assert out.shape == shape
def test_imexport_imimport(): shape = (2, 2) image = np.zeros(shape) pil_image = ndarray_to_pil(image) out = pil_to_ndarray(pil_image) assert out.shape == shape
def roundtrip_pil_image(self, x): pil_image = ndarray_to_pil(x) y = pil_to_ndarray(pil_image) return y
draw.text((10, 60),"This Homework is cooly cool",(255), font=font) draw.text((10, 85),"This Homework is cooly cool",(255), font=font) draw.text((10, 110),"This Homework is cooly cool",(255), font=font) draw.text((10, 135),"This Homework is cooly cool",(255), font=font) draw.text((10, 160),"This Homework is cooly cool",(255), font=font) draw.text((10, 185),"This Homework is cooly cool",(255), font=font) draw.text((10, 210),"This Homework is cooly cool",(255), font=font) draw.text((10, 235),"This Homework is cooly cool",(255), font=font) draw.text((10, 260),"This Homework is cooly cool",(255), font=font) y_test_missing.save('y_test_missing.png') #Building and saving the mask mask = ImageChops.difference(y_test_missing, y_test) mask = np.array(pil_to_ndarray(mask) <= 0.1, dtype=int) mask_img = mask * 255 mask = ndarray_to_pil(mask) mask_img = ndarray_to_pil(mask_img) mask_img.save('mask.png', 'PNG') psnr_y_test_y_test_missing = peak_signal_noise_ratio(pil_to_ndarray(y_test), pil_to_ndarray(y_test_missing)) ssim_y_test_y_test_missing = structural_similarity(pil_to_ndarray(y_test), pil_to_ndarray(y_test_missing)) print(psnr_y_test_y_test_missing) print(ssim_y_test_y_test_missing) print('\n') """ plt.imshow(y_train, cmap='gray') plt.show() plt.imshow(y_test, cmap='gray') plt.show()