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predict.py
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predict.py
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import cog
import tempfile
from pathlib import Path
import argparse
import sys
import shutil
import os
import cv2
import glob
from run import run_cmd
class Predictor(cog.Predictor):
def setup(self):
parser = argparse.ArgumentParser()
parser.add_argument("--input_folder", type=str, default='input/cog_temp', help="Test images")
parser.add_argument(
"--output_folder",
type=str,
default="output",
help="Restored images, please use the absolute path",
)
parser.add_argument("--GPU", type=str, default="0", help="0,1,2")
parser.add_argument(
"--checkpoint_name", type=str, default="Setting_9_epoch_100", help="choose which checkpoint"
)
self.opts = parser.parse_args('')
self.basepath = os.getcwd()
self.opts.input_folder = os.path.join(self.basepath, self.opts.input_folder)
self.opts.output_folder = os.path.join(self.basepath, self.opts.output_folder)
os.makedirs(self.opts.input_folder, exist_ok=True)
os.makedirs(self.opts.output_folder, exist_ok=True)
@cog.input("image", type=Path, help="input image")
@cog.input("HR", type=bool, default=False, help="whether high-resolution image")
@cog.input("with_scratch", type=bool, default=False, help="whether input image is scratched")
def predict(self, image, HR=False, with_scratch=False):
os.chdir(self.basepath)
input_path = os.path.join(self.opts.input_folder, os.path.basename(image))
shutil.copy(str(image), input_path)
gpu1 = self.opts.GPU
## Stage 1: Overall Quality Improve
print("Running Stage 1: Overall restoration")
os.chdir("./Global")
stage_1_input_dir = self.opts.input_folder
stage_1_output_dir = os.path.join(self.opts.output_folder, "stage_1_restore_output")
os.makedirs(stage_1_output_dir, exist_ok=True)
if not with_scratch:
stage_1_command = (
"python test.py --test_mode Full --Quality_restore --test_input "
+ stage_1_input_dir
+ " --outputs_dir "
+ stage_1_output_dir
+ " --gpu_ids "
+ gpu1
)
run_cmd(stage_1_command)
else:
mask_dir = os.path.join(stage_1_output_dir, "masks")
new_input = os.path.join(mask_dir, "input")
new_mask = os.path.join(mask_dir, "mask")
stage_1_command_1 = (
"python detection.py --test_path "
+ stage_1_input_dir
+ " --output_dir "
+ mask_dir
+ " --input_size full_size"
+ " --GPU "
+ gpu1
)
if HR:
HR_suffix = " --HR"
else:
HR_suffix = ""
stage_1_command_2 = (
"python test.py --Scratch_and_Quality_restore --test_input "
+ new_input
+ " --test_mask "
+ new_mask
+ " --outputs_dir "
+ stage_1_output_dir
+ " --gpu_ids "
+ gpu1 + HR_suffix
)
run_cmd(stage_1_command_1)
run_cmd(stage_1_command_2)
## Solve the case when there is no face in the old photo
stage_1_results = os.path.join(stage_1_output_dir, "restored_image")
stage_4_output_dir = os.path.join(self.opts.output_folder, "final_output")
os.makedirs(stage_4_output_dir, exist_ok=True)
for x in os.listdir(stage_1_results):
img_dir = os.path.join(stage_1_results, x)
shutil.copy(img_dir, stage_4_output_dir)
print("Finish Stage 1 ...")
print("\n")
## Stage 2: Face Detection
print("Running Stage 2: Face Detection")
os.chdir(".././Face_Detection")
stage_2_input_dir = os.path.join(stage_1_output_dir, "restored_image")
stage_2_output_dir = os.path.join(self.opts.output_folder, "stage_2_detection_output")
os.makedirs(stage_2_output_dir, exist_ok=True)
stage_2_command = (
"python detect_all_dlib_HR.py --url " + stage_2_input_dir + " --save_url " + stage_2_output_dir
)
run_cmd(stage_2_command)
print("Finish Stage 2 ...")
print("\n")
## Stage 3: Face Restore
print("Running Stage 3: Face Enhancement")
os.chdir(".././Face_Enhancement")
stage_3_input_mask = "./"
stage_3_input_face = stage_2_output_dir
stage_3_output_dir = os.path.join(self.opts.output_folder, "stage_3_face_output")
os.makedirs(stage_3_output_dir, exist_ok=True)
self.opts.checkpoint_name = 'FaceSR_512'
stage_3_command = (
"python test_face.py --old_face_folder "
+ stage_3_input_face
+ " --old_face_label_folder "
+ stage_3_input_mask
+ " --tensorboard_log --name "
+ self.opts.checkpoint_name
+ " --gpu_ids "
+ gpu1
+ " --load_size 512 --label_nc 18 --no_instance --preprocess_mode resize --batchSize 1 --results_dir "
+ stage_3_output_dir
+ " --no_parsing_map"
)
run_cmd(stage_3_command)
print("Finish Stage 3 ...")
print("\n")
## Stage 4: Warp back
print("Running Stage 4: Blending")
os.chdir(".././Face_Detection")
stage_4_input_image_dir = os.path.join(stage_1_output_dir, "restored_image")
stage_4_input_face_dir = os.path.join(stage_3_output_dir, "each_img")
stage_4_output_dir = os.path.join(self.opts.output_folder, "final_output")
os.makedirs(stage_4_output_dir, exist_ok=True)
stage_4_command = (
"python align_warp_back_multiple_dlib_HR.py --origin_url "
+ stage_4_input_image_dir
+ " --replace_url "
+ stage_4_input_face_dir
+ " --save_url "
+ stage_4_output_dir
)
run_cmd(stage_4_command)
print("Finish Stage 4 ...")
print("\n")
print("All the processing is done. Please check the results.")
img_name = os.path.basename(str(image))
image_restore = cv2.imread(os.path.join(self.opts.output_folder, 'final_output', img_name))
out_path = Path(tempfile.mkdtemp()) / "out.png"
cv2.imwrite(str(out_path), image_restore)
clean_folder(self.opts.input_folder)
clean_folder(self.opts.output_folder)
return out_path
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))