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