def load_model(self):
self.model = PConvUnet()
self.model.load(
r"./models/model.h5",
train_bn=False,
lr=0.00005
)
def load_model(self):
self.model = PConvUnet(weight_filepath='data/logs/')
self.model.load(
r"./models/model.h5",
train_bn=False,
lr=0.00005
)
class Decensor:
def __init__(self):
self.args = config.get_args()
self.is_mosaic = self.args.is_mosaic
self.mask_color = [
self.args.mask_color_red / 255.0,
self.args.mask_color_green / 255.0,
self.args.mask_color_blue / 255.0
]
if not os.path.exists(self.args.decensor_output_path):
os.makedirs(self.args.decensor_output_path)
self.load_model()
def get_mask(self, colored):
mask = np.ones(colored.shape, np.uint8)
i, j = np.where(np.all(colored[0] == self.mask_color, axis=-1))
mask[0, i, j] = 0
return mask
def load_model(self):
self.model = PConvUnet(weight_filepath='data/logs/')
self.model.load(r"./models/model.h5", train_bn=False, lr=0.00005)
def decensor_all_images_in_folder(self):
#load model once at beginning and reuse same model
#self.load_model()
color_dir = self.args.decensor_input_path
file_names = os.listdir(color_dir)
#convert all images into np arrays and put them in a list
for file_name in file_names:
color_file_path = os.path.join(color_dir, file_name)
color_bn, color_ext = os.path.splitext(file_name)
if os.path.isfile(
color_file_path) and color_ext.casefold() == ".png":
print(
"--------------------------------------------------------------------------"
)
print("Decensoring the image {}".format(color_file_path))
colored_img = Image.open(color_file_path)
#if we are doing a mosaic decensor
if self.is_mosaic:
#get the original file that hasn't been colored
ori_dir = self.args.decensor_input_original_path
#since the original image might not be a png, test multiple file formats
valid_formats = {".png", ".jpg", ".jpeg"}
for test_file_name in os.listdir(ori_dir):
test_bn, test_ext = os.path.splitext(test_file_name)
if (test_bn == color_bn) and (test_ext.casefold()
in valid_formats):
ori_file_path = os.path.join(
ori_dir, test_file_name)
ori_img = Image.open(ori_file_path)
self.decensor_image(ori_img, colored_img,
file_name)
break
else: #for...else, i.e if the loop finished without encountering break
print(
"Corresponding original, uncolored image not found in {}."
.format(ori_file_path))
print(
"Check if it exists and is in the PNG or JPG format."
)
else:
self.decensor_image(colored_img, colored_img, file_name)
print(
"--------------------------------------------------------------------------"
)
#decensors one image at a time
#TODO: decensor all cropped parts of the same image in a batch (then i need input for colored an array of those images and make additional changes)
def decensor_image(self, ori, colored, file_name):
width, height = ori.size
#save the alpha channel if the image has an alpha channel
has_alpha = False
if (ori.mode == "RGBA"):
has_alpha = True
alpha_channel = np.asarray(ori)[:, :, 3]
alpha_channel = np.expand_dims(alpha_channel, axis=-1)
ori = ori.convert('RGB')
ori_array = np.asarray(ori)
ori_array = np.array(ori_array / 255.0)
ori_array = np.expand_dims(ori_array, axis=0)
if self.is_mosaic:
#if mosaic decensor, mask is empty
mask = np.ones(ori_array.shape, np.uint8)
else:
mask = self.get_mask(ori_array)
#colored image is only used for finding the regions
regions = find_regions(colored.convert('RGB'))
print("Found {region_count} censored regions in this image!".format(
region_count=len(regions)))
if len(regions) == 0 and not self.is_mosaic:
print("No green regions detected!")
return
output_img_array = ori_array[0].copy()
for region_counter, region in enumerate(regions, 1):
bounding_box = expand_bounding(ori, region)
crop_img = ori.crop(bounding_box)
# crop_img.show()
#convert mask back to image
mask_reshaped = mask[0, :, :, :] * 255.0
mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
#resize the cropped images
crop_img = crop_img.resize((512, 512))
crop_img_array = np.asarray(crop_img)
crop_img_array = crop_img_array / 255.0
crop_img_array = np.expand_dims(crop_img_array, axis=0)
#resize the mask images
mask_img = mask_img.crop(bounding_box)
mask_img = mask_img.resize((512, 512))
# mask_img.show()
#convert mask_img back to array
mask_array = np.asarray(mask_img)
mask_array = np.array(mask_array / 255.0)
#the mask has been upscaled so there will be values not equal to 0 or 1
#mask_array[mask_array < 0.01] = 0
mask_array[mask_array > 0] = 1
mask_array = np.expand_dims(mask_array, axis=0)
# Run predictions for this batch of images
pred_img_array = self.model.predict(
[crop_img_array, mask_array, mask_array])
pred_img_array = pred_img_array * 255.0
pred_img_array = np.squeeze(pred_img_array, axis=0)
#scale prediction image back to original size
bounding_width = bounding_box[2] - bounding_box[0]
bounding_height = bounding_box[3] - bounding_box[1]
#convert np array to image
# print(bounding_width,bounding_height)
# print(pred_img_array.shape)
pred_img = Image.fromarray(pred_img_array.astype('uint8'))
# pred_img.show()
pred_img = pred_img.resize((bounding_width, bounding_height),
resample=Image.BICUBIC)
pred_img_array = np.asarray(pred_img)
pred_img_array = pred_img_array / 255.0
# print(pred_img_array.shape)
pred_img_array = np.expand_dims(pred_img_array, axis=0)
for i in range(len(ori_array)):
for col in range(bounding_width):
for row in range(bounding_height):
bounding_width_index = col + bounding_box[0]
bounding_height_index = row + bounding_box[1]
if (bounding_width_index,
bounding_height_index) in region:
output_img_array[bounding_height_index][
bounding_width_index] = pred_img_array[
i, :, :, :][row][col]
print("{region_counter} out of {region_count} regions decensored.".
format(region_counter=region_counter,
region_count=len(regions)))
output_img_array = output_img_array * 255.0
#restore the alpha channel
if has_alpha:
#print(output_img_array.shape)
#print(alpha_channel.shape)
output_img_array = np.concatenate(
(output_img_array, alpha_channel), axis=2)
output_img = Image.fromarray(output_img_array.astype('uint8'))
#save the decensored image
#file_name, _ = os.path.splitext(file_name)
save_path = os.path.join(self.args.decensor_output_path, file_name)
output_img.save(save_path)
print("Decensored image saved to {save_path}!".format(
save_path=save_path))
return
class Decensor:
def __init__(self):
self.args = config.get_args()
self.is_mosaic = self.args.is_mosaic
self.mask_color = [float(v / 255) for v in self.args.mask_color
] # normalize mask color
if not os.path.exists(self.args.decensor_output_path):
os.makedirs(self.args.decensor_output_path)
self.load_model()
def get_mask(self, colored):
mask = np.ones(colored.shape, np.uint8)
i, j = np.where(np.all(colored[0] == self.mask_color, axis=-1))
mask[0, i, j] = 0
return mask
def load_model(self):
self.model = PConvUnet()
self.model.load(r"./models/model.h5", train_bn=False, lr=0.00005)
def decensor_all_images_in_folder(self):
#load model once at beginning and reuse same model
#self.load_model()
color_dir = self.args.decensor_input_path
file_names = os.listdir(color_dir)
input_dir = self.args.decensor_input_path
output_dir = self.args.decensor_output_path
# Change False to True before release --> file.check_file( input_dir, output_dir, True)
file_names, self.files_removed = file.check_file(
input_dir, output_dir, False)
#convert all images into np arrays and put them in a list
for file_name in file_names:
color_file_path = os.path.join(color_dir, file_name)
color_bn, color_ext = os.path.splitext(file_name)
if os.path.isfile(
color_file_path) and color_ext.casefold() == ".png":
print(
"--------------------------------------------------------------------------"
)
print("Decensoring the image {}".format(color_file_path))
try:
colored_img = Image.open(color_file_path)
except:
print("Cannot identify image file (" +
str(color_file_path) + ")")
self.files_removed.append((color_file_path, 3))
# incase of abnormal file format change (ex : text.txt -> text.png)
continue
#if we are doing a mosaic decensor
if self.is_mosaic:
#get the original file that hasn't been colored
ori_dir = self.args.decensor_input_original_path
#since the original image might not be a png, test multiple file formats
valid_formats = {".png", ".jpg", ".jpeg"}
for test_file_name in os.listdir(ori_dir):
test_bn, test_ext = os.path.splitext(test_file_name)
if (test_bn == color_bn) and (test_ext.casefold()
in valid_formats):
ori_file_path = os.path.join(
ori_dir, test_file_name)
ori_img = Image.open(ori_file_path)
# colored_img.show()
self.decensor_image(ori_img, colored_img,
file_name)
break
else: #for...else, i.e if the loop finished without encountering break
print(
"Corresponding original, uncolored image not found in {}"
.format(color_file_path))
print(
"Check if it exists and is in the PNG or JPG format."
)
else:
self.decensor_image(colored_img, colored_img, file_name)
else:
print(
"--------------------------------------------------------------------------"
)
print("Iregular file deteced : " + str(color_file_path))
print(
"--------------------------------------------------------------------------"
)
if (self.files_removed is not None):
file.error_messages(None, self.files_removed)
input("\nPress anything to end...")
#decensors one image at a time
#TODO: decensor all cropped parts of the same image in a batch (then i need input for colored an array of those images and make additional changes)
def decensor_image(self, ori, colored, file_name=None):
width, height = ori.size
#save the alpha channel if the image has an alpha channel
has_alpha = False
if (ori.mode == "RGBA"):
has_alpha = True
alpha_channel = np.asarray(ori)[:, :, 3]
alpha_channel = np.expand_dims(alpha_channel, axis=-1)
ori = ori.convert('RGB')
ori_array = image_to_array(ori)
ori_array = np.expand_dims(ori_array, axis=0)
if self.is_mosaic:
#if mosaic decensor, mask is empty
# mask = np.ones(ori_array.shape, np.uint8)
# print(mask.shape)
colored = colored.convert('RGB')
color_array = image_to_array(colored)
color_array = np.expand_dims(color_array, axis=0)
mask = self.get_mask(color_array)
# mask_reshaped = mask[0,:,:,:] * 255.0
# mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
# mask_img.show()
else:
mask = self.get_mask(ori_array)
#colored image is only used for finding the regions
regions = find_regions(
colored.convert('RGB'),
[v * 255 for v in self.mask_color
]) #unnormalize the color so it can check against pixels
print("Found {region_count} censored regions in this image!".format(
region_count=len(regions)))
if len(regions) == 0 and not self.is_mosaic:
print(
"No green regions detected! Make sure you're using exactly the right color."
)
return
output_img_array = ori_array[0].copy()
for region_counter, region in enumerate(regions, 1):
bounding_box = expand_bounding(ori, region)
crop_img = ori.crop(bounding_box)
# crop_img.show()
#convert mask back to image
mask_reshaped = mask[0, :, :, :] * 255.0
mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
#resize the cropped images
crop_img = crop_img.resize((512, 512))
crop_img_array = image_to_array(crop_img)
crop_img_array = np.expand_dims(crop_img_array, axis=0)
#resize the mask images
mask_img = mask_img.crop(bounding_box)
mask_img = mask_img.resize((512, 512))
# mask_img.show()
#convert mask_img back to array
mask_array = image_to_array(mask_img)
#the mask has been upscaled so there will be values not equal to 0 or 1
mask_array[mask_array > 0] = 1
if self.is_mosaic:
a, b = np.where(np.all(mask_array == 0, axis=-1))
print(a, b)
coords = [
coord for coord in zip(a, b)
if ((coord[0] + coord[1]) % 2 == 0)
]
a, b = zip(*coords)
mask_array[a, b] = 1
# mask_array = mask_array * 255.0
# img = Image.fromarray(mask_array.astype('uint8'))
# img.show()
# return
mask_array = np.expand_dims(mask_array, axis=0)
# Run predictions for this batch of images
pred_img_array = self.model.predict(
[crop_img_array, mask_array, mask_array])
pred_img_array = pred_img_array * 255.0
pred_img_array = np.squeeze(pred_img_array, axis=0)
#scale prediction image back to original size
bounding_width = bounding_box[2] - bounding_box[0]
bounding_height = bounding_box[3] - bounding_box[1]
#convert np array to image
# print(bounding_width,bounding_height)
# print(pred_img_array.shape)
pred_img = Image.fromarray(pred_img_array.astype('uint8'))
# pred_img.show()
pred_img = pred_img.resize((bounding_width, bounding_height),
resample=Image.BICUBIC)
pred_img_array = image_to_array(pred_img)
# print(pred_img_array.shape)
pred_img_array = np.expand_dims(pred_img_array, axis=0)
# copy the decensored regions into the output image
for i in range(len(ori_array)):
for col in range(bounding_width):
for row in range(bounding_height):
bounding_width_index = col + bounding_box[0]
bounding_height_index = row + bounding_box[1]
if (bounding_width_index,
bounding_height_index) in region:
output_img_array[bounding_height_index][
bounding_width_index] = pred_img_array[
i, :, :, :][row][col]
print("{region_counter} out of {region_count} regions decensored.".
format(region_counter=region_counter,
region_count=len(regions)))
output_img_array = output_img_array * 255.0
#restore the alpha channel if the image had one
if has_alpha:
output_img_array = np.concatenate(
(output_img_array, alpha_channel), axis=2)
output_img = Image.fromarray(output_img_array.astype('uint8'))
if file_name != None:
#save the decensored image
#file_name, _ = os.path.splitext(file_name)
save_path = os.path.join(self.args.decensor_output_path, file_name)
output_img.save(save_path)
print("Decensored image saved to {save_path}!".format(
save_path=save_path))
return
else:
print("Decensored image. Returning it.")
return output_img
def load_model(self):
self.model = PConvUnet(weight_filepath='data/logs/')
self.model.load(r"/content/drive/My Drive/DeepCreamPy/models/model.h5",
train_bn=False,
lr=0.00005)