From 0e218f46fd07cbaf6a904c38cd43dac0148d9dc9 Mon Sep 17 00:00:00 2001 From: Michael Date: Mon, 7 Oct 2024 15:39:57 +0100 Subject: [PATCH] sr1 --- modules/processors/frame/super_resolution.py | 197 +++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 modules/processors/frame/super_resolution.py diff --git a/modules/processors/frame/super_resolution.py b/modules/processors/frame/super_resolution.py new file mode 100644 index 0000000..13d9a5f --- /dev/null +++ b/modules/processors/frame/super_resolution.py @@ -0,0 +1,197 @@ +import threading +import traceback +from typing import Any, List +import cv2 + +import os + +import modules.globals +import modules.processors.frame.core +from modules.core import update_status +from modules.face_analyser import get_one_face +from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video +import numpy as np + +NAME = 'DLC.SUPER-RESOLUTION' +THREAD_SEMAPHORE = threading.Semaphore() + +# Singleton class for Super-Resolution +class SuperResolutionModel: + _instance = None + _lock = threading.Lock() + + def __init__(self, sr_model_path: str = f'ESPCN_x{modules.globals.sr_scale_factor}.pb'): + if SuperResolutionModel._instance is not None: + raise Exception("This class is a singleton!") + self.sr = cv2.dnn_superres.DnnSuperResImpl_create() + self.model_path = os.path.join(resolve_relative_path('../models'), sr_model_path) + if not os.path.exists(self.model_path): + raise FileNotFoundError(f"Super-resolution model not found at {self.model_path}") + try: + self.sr.readModel(self.model_path) + self.sr.setModel("espcn", modules.globals.sr_scale_factor) # Using ESPCN with 2,3 or 4x upscaling + except Exception as e: + print(f"Error during super-resolution model initialization: {e}") + raise e + + @classmethod + def get_instance(cls, sr_model_path: str = f'ESPCN_x{modules.globals.sr_scale_factor}.pb'): + if cls._instance is None: + with cls._lock: + if cls._instance is None: + try: + cls._instance = cls(sr_model_path) + except Exception as e: + raise RuntimeError(f"Failed to initialize SuperResolution: {str(e)}") + return cls._instance + + +def pre_check() -> bool: + """ + Checks and downloads necessary models before starting the face swapper. + """ + download_directory_path = resolve_relative_path('../models') + # Download the super-resolution model as well + conditional_download(download_directory_path, [ + f'https://huggingface.co/spaces/PabloGabrielSch/AI_Resolution_Upscaler_And_Resizer/resolve/bcd13b766a9499196e8becbe453c4a848673b3b6/models/ESPCN_x{modules.globals.sr_scale_factor}.pb' + ]) + return True + +def pre_start() -> bool: + if not is_image(modules.globals.source_path): + update_status('Select an image for source path.', NAME) + return False + elif not get_one_face(cv2.imread(modules.globals.source_path)): + update_status('No face detected in the source path.', NAME) + return False + if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path): + update_status('Select an image or video for target path.', NAME) + return False + return True + + +def apply_super_resolution(image: np.ndarray) -> np.ndarray: + """ + Applies super-resolution to the given image using the provided super-resolver. + + Args: + image (np.ndarray): The input image to enhance. + sr_model_path (str): ESPCN model path for super-resolution. + + Returns: + np.ndarray: The super-resolved image. + """ + with THREAD_SEMAPHORE: + sr_model = SuperResolutionModel.get_instance() + + if sr_model is None: + print("Super-resolution model is not initialized.") + return image + try: + upscaled_image = sr_model.sr.upsample(image) + return upscaled_image + except Exception as e: + print(f"Error during super-resolution: {e}") + return image + + +def process_frame(frame: np.ndarray) -> np.ndarray: + """ + Processes a single frame by swapping the source face into detected target faces. + + Args: + + frame (np.ndarray): The target frame image. + + Returns: + np.ndarray: The processed frame with swapped faces. + """ + + # Apply super-resolution to the entire frame + frame = apply_super_resolution(frame) + + return frame + +def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None: + """ + Processes multiple frames by swapping the source face into each target frame. + + Args: + source_path (str): Path to the source image. + temp_frame_paths (List[str]): List of paths to target frame images. + progress (Any, optional): Progress tracker. Defaults to None. + """ + for idx, temp_frame_path in enumerate(temp_frame_paths): + frame = cv2.imread(temp_frame_path) + if frame is None: + print(f"Failed to load frame from {temp_frame_path}") + continue + try: + result = process_frame(frame) + cv2.imwrite(temp_frame_path, result) + except Exception as exception: + traceback.print_exc() + print(f"Error processing frame {temp_frame_path}: {exception}") + if progress: + progress.update(1) + +def upscale_image(image: np.ndarray, scaling_factor: int = 2) -> np.ndarray: + """ + Upscales the given image by the specified scaling factor. + + Args: + image (np.ndarray): The input image to upscale. + scaling_factor (int): The factor by which to upscale the image. + + Returns: + np.ndarray: The upscaled image. + """ + height, width = image.shape[:2] + new_size = (width * scaling_factor, height * scaling_factor) + upscaled_image = cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC) + return upscaled_image + +def process_image(source_path: str, target_path: str, output_path: str) -> None: + """ + Processes a single image by swapping the source face into the target image. + + Args: + source_path (str): Path to the source image. + target_path (str): Path to the target image. + output_path (str): Path to save the output image. + """ + source_image = cv2.imread(source_path) + if source_image is None: + print(f"Failed to load source image from {source_path}") + return + + # Upscale the source image for better quality before face detection + source_image_upscaled = upscale_image(source_image, scaling_factor=2) + + # Detect source face from the upscaled image + source_face = get_one_face(source_image_upscaled) + if source_face is None: + print("No source face detected.") + return + + target_frame = cv2.imread(target_path) + if target_frame is None: + print(f"Failed to load target image from {target_path}") + return + + # Process the frame + result = process_frame(target_frame) + + # Save the processed frame + cv2.imwrite(output_path, result) + + +def process_video(source_path: str, temp_frame_paths: List[str]) -> None: + """ + Processes a video by swapping the source face into each frame. + + Args: + source_path (str): Path to the source image. + temp_frame_paths (List[str]): List of paths to video frame images. + """ + modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames) \ No newline at end of file