def original_color_transform(content, generated, mask=None):
generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr') # Convert to YCbCr color space
if mask is None:
generated[:, :, 1:] = content[:, :, 1:] # Generated CbCr = Content CbCr
else:
width, height, channels = generated.shape
for i in range(width):
for j in range(height):
if mask[i, j] == 1:
generated[i, j, 1:] = content[i, j, 1:]
generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB') # Convert to RGB color space
return generated
def test(self, config):
"""
Output: bicubic image and SRCNN image pairs
"""
self.loadModel()
image_paths = prepare_data(self.sess, dataset="Test")
avg_psnr_srcnn = 0
avg_psnr_bicubic = 0
for image_path in image_paths:
image_dir, image_name = os.path.split(image_path)
nx, ny, bicubic_img, ground_truth = input_setup(self.sess, config, image_path)
data_dir = os.path.join('./{}'.format(config.checkpoint_dir), "test.h5")
train_data, train_label = read_data(data_dir) # train_data(bicubic):(33, 33); train_label(gt):(21, 21)
result = self.pred.eval({self.images: train_data, self.labels: train_label})
PSNR_bicubic = PSNR(train_data, train_label)
PSNR_srcnn = PSNR(result, train_label)
avg_psnr_bicubic += PSNR_bicubic
avg_psnr_srcnn += PSNR_srcnn
result = merge(result, [nx, ny]) # result(SRCNN):(21, 21)
result = result.squeeze()
image_dir = os.path.join(os.getcwd(), config.sample_dir)
image_path = os.path.join(image_dir, image_name)
bicubic_path = os.path.join(image_dir, "bicubic_"+image_name)
px = 1/plt.rcParams['figure.dpi'] # pixel in inches
width = max(ground_truth.shape[0], ground_truth.shape[1])
# plot image
plt.figure(image_name, figsize=(2*width*px,3*width*px))
ax1 = plt.subplot(3,1,1)
ax1.set_title("SRCNN - PSNR - " + str(PSNR_srcnn))
plt.imshow(toimage(result), cmap='gray')
ax2 = plt.subplot(3,1,2)
ax2.set_title("Bicubic - PSNR - " + str(PSNR_bicubic))
plt.imshow(toimage(bicubic_img), cmap='gray')
ax3 = plt.subplot(3,1,3)
ax3.set_title("Ground Truth")
plt.imshow(toimage(ground_truth), cmap='gray')
plt.savefig(image_path)
plt.close()
avg_psnr_srcnn /= len(image_paths)
avg_psnr_bicubic /= len(image_paths)
print("average PSNR of srcnn = {}\n average PSNR of bicubic = {}".format(avg_psnr_srcnn, avg_psnr_bicubic))
def original_color_transform(content, generated, mask=None, hist_match=0, mode='YCbCr'):
generated = fromimage(toimage(generated, mode='RGB'), mode=mode) # Convert to YCbCr color space
if mask is None:
if hist_match == 1:
for channel in range(3):
generated[:, :, channel] = match_histograms(generated[:, :, channel], content[:, :, channel])
else:
generated[:, :, 1:] = content[:, :, 1:]
else:
width, height, channels = generated.shape
for i in range(width):
for j in range(height):
if mask[i, j] == 1:
if hist_match == 1:
for channel in range(3):
generated[i, j, channel] = match_histograms(generated[i, j, channel], content[i, j, channel])
else:
generated[i, j, 1:] = content[i, j, 1:]
generated = fromimage(toimage(generated, mode=mode), mode='RGB') # Convert to RGB color space
return generated
for trgimg in targetfiles:
if trgimg.endswith('.png'):
targetimgs.append(trgimg)
# Start converting
num_source_img = 0
for srcimg in sourceimgs:
num_source_img += 1
im_src = Image.open(sourcepath + srcimg).convert('RGB')
if RANDOM_TARGET:
random_tar = random.choice(targetimgs)
elif GUIDED_TARGET:
random_tar = matcher_dict[srcimg]
im_trg = Image.open(targetpath + random_tar).convert('RGB')
w, h = im_src.size[0], im_src.size[1]
im_src = im_src.resize((w, h), Image.BICUBIC)
im_trg = im_trg.resize((w, h), Image.BICUBIC)
im_src = np.asarray(im_src, np.float32)
im_trg = np.asarray(im_trg, np.float32)
im_src = im_src.transpose((2, 0, 1))
im_trg = im_trg.transpose((2, 0, 1))
src_in_trg = FDA_source_to_target_np(im_src, im_trg, L=0.01)
src_in_trg = src_in_trg.transpose((1, 2, 0))
print('converting: ', srcimg, 'target: ', random_tar,
"(" + str(num_source_img) + ")")
toimage(src_in_trg, cmin=0.0, cmax=255.0).save(savepath + srcimg)
