import os import sys import subprocess # --- CẤU HÌNH PATH NGAY LẬP TỨC --- sys.path.append(os.getcwd()) # --- PHẦN 1: SETUP MÔI TRƯỜNG (FINAL FIX) --- print("⏳ Đang thiết lập môi trường...") # 0. CÀI ĐẶT CÁC THƯ VIỆN BỊ THIẾU (BẮT BUỘC) # DRCT_arch yêu cầu einops nhưng chưa có trong requirements.txt print(" + Installing missing dependencies (einops)...") subprocess.run([sys.executable, "-m", "pip", "install", "einops", "scipy"], check=True) # 1. Clone CodeFormer if not os.path.exists("CodeFormer"): print(" + Cloning CodeFormer...") subprocess.run(["git", "clone", "https://github.com/sczhou/CodeFormer.git"], check=True) # 2. TẠO CÁC FILE GIẢ LẬP ĐỂ TRÁNH LỖI SETUP.PY # Setup.py của BasicSR rất "khó tính", nó đòi hỏi file VERSION phải tồn tại ở đúng chỗ print(" + Creating dummy version files...") # Tạo file VERSION (Fix lỗi FileNotFoundError: './basicsr/VERSION') if not os.path.exists("CodeFormer/basicsr/VERSION"): with open("CodeFormer/basicsr/VERSION", "w", encoding="utf-8") as f: f.write("1.4.2") # Tạo file version.py đầy đủ (Fix lỗi ImportError: cannot import name '__gitsha__') version_py_path = "CodeFormer/basicsr/version.py" with open(version_py_path, "w", encoding="utf-8") as f: f.write("version = '1.4.2'\n") f.write("__gitsha__ = 'unknown'\n") f.write("__version__ = '1.4.2'\n") # Patch setup.py (Phòng hờ) setup_file_path = "CodeFormer/basicsr/setup.py" if os.path.exists(setup_file_path): with open(setup_file_path, "r", encoding="utf-8") as f: content = f.read() content = content.replace("version=get_version(),", "version='1.4.2',") with open(setup_file_path, "w", encoding="utf-8") as f: f.write(content) # 3. CÀI ĐẶT BASICSR print(" + Installing BasicSR...") if not os.path.exists("CodeFormer/basicsr.egg-info"): try: # --no-build-isolation: Dùng torch có sẵn # --no-deps: Không cài lại torch subprocess.run( [sys.executable, "-m", "pip", "install", ".", "--no-build-isolation", "--no-deps"], cwd="CodeFormer/basicsr", check=True ) except subprocess.CalledProcessError: print("⚠️ Cài đặt BasicSR thất bại. Chuyển sang chế độ chạy trực tiếp (Pure Python).") # 4. CÀI ĐẶT GFPGAN print(" + Installing GFPGAN...") try: import gfpgan except ImportError: subprocess.run([sys.executable, "-m", "pip", "install", "gfpgan", "--no-deps"], check=True) # Thêm CodeFormer vào path sys.path.append(os.path.join(os.getcwd(), "CodeFormer")) # ----------------------------------------------------------- import gradio as gr import torch import cv2 import time import numpy as np from PIL import Image, ImageEnhance from torchvision.transforms.functional import normalize # Import module an toàn try: from basicsr.utils import img2tensor, tensor2img from basicsr.utils.realesrgan_utils import RealESRGANer from basicsr.utils.download_util import load_file_from_url from basicsr.archs.codeformer_arch import CodeFormer from facelib.utils.face_restoration_helper import FaceRestoreHelper except ImportError as e: print(f"⚠️ Lỗi Import BasicSR: {e}. Đang kiểm tra lại path...") sys.path.append(os.path.join(os.getcwd(), "CodeFormer")) try: from basicsr.utils import img2tensor, tensor2img from basicsr.utils.realesrgan_utils import RealESRGANer from basicsr.utils.download_util import load_file_from_url from basicsr.archs.codeformer_arch import CodeFormer from facelib.utils.face_restoration_helper import FaceRestoreHelper except ImportError as e2: print(f"❌ Lỗi Import nghiêm trọng: {e2}") # --- CẤU HÌNH --- DRCT_MODEL_PATH = "Real_DRCT_GAN_SRx4_finetuned_from_mse_net_g_latest.pth" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # --- CLASS TÙY CHỈNH --- class RealESRGANer_Custom(RealESRGANer): def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False, device=None, gpu_id=None): self.scale = scale self.tile_size = tile self.tile_pad = tile_pad self.pre_pad = pre_pad self.mod_scale = 16 self.half = half if device is None: self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') else: self.device = device if model_path is not None: if model_path.startswith('https://'): model_path = load_file_from_url( url=model_path, model_dir=os.path.join('weights/realesrgan'), progress=True, file_name=None) loadnet = torch.load(model_path, map_location=torch.device('cpu')) keyname = 'params_ema' if 'params_ema' in loadnet else 'params' model.load_state_dict(loadnet[keyname], strict=True) model.eval() self.model = model.to(self.device) if self.half: self.model = self.model.half() def pre_process(self, img): img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float() self.img = img.unsqueeze(0).to(self.device) if self.half: self.img = self.img.half() if self.pre_pad != 0: self.img = torch.nn.functional.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect') if self.mod_scale is not None: self.mod_pad_h, self.mod_pad_w = 0, 0 _, _, h, w = self.img.size() if (h % self.mod_scale != 0): self.mod_pad_h = (self.mod_scale - h % self.mod_scale) if (w % self.mod_scale != 0): self.mod_pad_w = (self.mod_scale - w % self.mod_scale) self.img = torch.nn.functional.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect') def load_drct_model(model_path, device): try: from DRCT_arch import DRCT except ImportError as e: print(f"Lỗi import DRCT: {e}") # Thử import lại nếu einops vừa mới được cài import site site.main() try: from DRCT_arch import DRCT except ImportError: raise ImportError("❌ Không thể import class 'DRCT'. Đảm bảo đã cài 'einops'.") model = DRCT( upscale=4, in_chans=3, img_size=64, window_size=16, compress_ratio=3, squeeze_factor=30, conv_scale=0.01, overlap_ratio=0.5, img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffle', resi_connection='1conv' ) if not os.path.exists(model_path): raise FileNotFoundError(f"Thiếu file model weights: {model_path}") checkpoint = torch.load(model_path, map_location=device) state_dict = checkpoint['params_ema'] if 'params_ema' in checkpoint else checkpoint['params'] model.load_state_dict(state_dict, strict=False) model.eval() return model.to(device) # --- LOAD MODEL --- print("⏳ Đang tải Model...") drct_model = None codeformer = None try: drct_model = load_drct_model(DRCT_MODEL_PATH, device) if not os.path.exists('weights/CodeFormer/codeformer.pth'): os.makedirs('weights/CodeFormer', exist_ok=True) load_file_from_url(url='https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth', model_dir='weights/CodeFormer', progress=True, file_name='codeformer.pth') codeformer = CodeFormer(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(device) ckpt = torch.load('weights/CodeFormer/codeformer.pth')['params_ema'] codeformer.load_state_dict(ckpt) codeformer.eval() print("✅ Model Ready!") except Exception as e: print(f"⚠️ Lỗi khởi tạo Model: {e}") import traceback traceback.print_exc() # --- XỬ LÝ ẢNH --- def process_image(input_img, w=0.7): if drct_model is None: return None, None, "Lỗi Model (Xem Logs)", "" if input_img is None: return None, None, "Thiếu ảnh input", "" img = cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR) # 1. DRCT torch.cuda.empty_cache() if torch.cuda.is_available(): torch.cuda.reset_peak_memory_stats() start_time = time.time() try: upsampler = RealESRGANer_Custom( scale=4, model_path=None, model=drct_model, tile=512, tile_pad=32, pre_pad=0, half=False, device=device ) if device.type == 'cuda': with torch.autocast(device_type='cuda', dtype=torch.float16): bg_img, _ = upsampler.enhance(img, outscale=4) else: bg_img, _ = upsampler.enhance(img, outscale=4) except Exception as e: return None, None, f"Lỗi DRCT: {str(e)}", "" drct_time = time.time() - start_time drct_vram = 0 if torch.cuda.is_available(): drct_vram = torch.cuda.max_memory_allocated() / (1024 ** 3) res_drct = cv2.cvtColor(bg_img, cv2.COLOR_BGR2RGB) stats_drct = f"⏱️ {drct_time:.2f}s | 💾 {drct_vram:.2f} GB | 📏 {bg_img.shape[1]}x{bg_img.shape[0]}" # 2. CODEFORMER if torch.cuda.is_available(): torch.cuda.reset_peak_memory_stats() start_time_cf = time.time() try: face_helper = FaceRestoreHelper( upscale_factor=4, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=device ) face_helper.clean_all() face_helper.read_image(img) face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) face_helper.align_warp_face() # Xử lý khuôn mặt for idx, cropped_face in enumerate(face_helper.cropped_faces): cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) cropped_face_t = cropped_face_t.unsqueeze(0).to(device) with torch.no_grad(): output = codeformer(cropped_face_t, w=w, adain=True)[0] restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) face_helper.add_restored_face(restored_face) face_helper.get_inverse_affine(None) final_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=False) # Chuyển BGR (OpenCV) sang RGB để xử lý với PIL và hiển thị trên UI final_img_rgb = cv2.cvtColor(final_img, cv2.COLOR_BGR2RGB) final_img_pil = Image.fromarray(final_img_rgb) # Thực hiện Enhance (nếu cần) final_img_pil = ImageEnhance.Color(final_img_pil).enhance(1.0) final_img_pil = ImageEnhance.Contrast(final_img_pil).enhance(1.0) # Chuyển về mảng numpy để Gradio hiển thị đúng màu res_hybrid = np.array(final_img_pil) except Exception as e: print(f"CodeFormer Error/No Face: {e}") res_hybrid = res_drct stats_hybrid = f"⚠️ Lỗi CF/Không có mặt: {str(e)}" return res_drct, res_hybrid, stats_drct, stats_hybrid cf_time = time.time() - start_time_cf total_time = drct_time + cf_time max_vram = drct_vram if torch.cuda.is_available(): max_vram = max(drct_vram, torch.cuda.max_memory_allocated() / (1024 ** 3)) stats_hybrid = (f"⏱️ Tổng: {total_time:.2f}s\n" f" (DRCT: {drct_time:.2f}s + CF: {cf_time:.2f}s)\n" f"💾 VRAM Peak: {max_vram:.2f} GB") return res_drct, res_hybrid, stats_drct, stats_hybrid # --- UI --- title = "So sánh Upscale: DRCT vs Hybrid" with gr.Blocks(title=title) as demo: gr.Markdown(f"# {title}") with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="Input") w_slider = gr.Slider(0.1, 1.0, 0.7, step=0.1, label="CodeFormer Weight (0=Restore, 1=Identity)") run_btn = gr.Button("🚀 Chạy", variant="primary") with gr.Row(): with gr.Column(): output_drct = gr.Image(label="DRCT Only") stats_drct_box = gr.Textbox(label="Stats") with gr.Column(): output_hybrid = gr.Image(label="DRCT + CodeFormer") stats_hybrid_box = gr.Textbox(label="Stats") run_btn.click(process_image, [input_image, w_slider], [output_drct, output_hybrid, stats_drct_box, stats_hybrid_box]) if __name__ == "__main__": demo.queue().launch()