Update main.py

main.py

@@ -10,87 +10,124 @@ from gfpgan.utils import GFPGANer
 from realesrgan.utils import RealESRGANer
 
 app = FastAPI()
-app.mount("/static", StaticFiles(directory="static"), name="static")
-templates = Jinja2Templates(directory="templates")
-
-# Download weights if not exists
-def download_weights():
-    weights = [
-        ('realesr-general-x4v3.pth', 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'),
-        ('GFPGANv1.2.pth', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth'),
-        ('GFPGANv1.3.pth', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'),
-        ('GFPGANv1.4.pth', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth')
-    ]
-    for weight_file, weight_url in weights:
-        if not os.path.exists(weight_file):
-            os.system(f"wget {weight_url} -P .")
-
-# Initialize model and weights
-def initialize_models():
-    model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-    half = True if torch.cuda.is_available() else False
-    return model, half
-
-# Perform image enhancement
-def enhance_image(img_path, version, scale, model, half):
+os.system("pip freeze")
+# download weights
+if not os.path.exists('realesr-general-x4v3.pth'):
+    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
+if not os.path.exists('GFPGANv1.2.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
+if not os.path.exists('GFPGANv1.3.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
+if not os.path.exists('GFPGANv1.4.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
+
+
+torch.hub.download_url_to_file(
+    'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
+    'a1.jpg')
+torch.hub.download_url_to_file(
+    'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
+    'a2.jpg')
+torch.hub.download_url_to_file(
+    'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202',
+    'a3.jpg')
+torch.hub.download_url_to_file(
+    'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
+    'a4.jpg')
+
+# background enhancer with RealESRGAN
+model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+model_path = 'realesr-general-x4v3.pth'
+half = True if torch.cuda.is_available() else False
+upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
+
+os.makedirs('output', exist_ok=True)
+
+
+# def inference(img, version, scale, weight):
+def inference(img, version, scale):
+    # weight /= 100
+    print(img, version, scale)
     try:
-        input_img = cv2.imread(img_path)
-        face_enhancer = None
-        
+        extension = os.path.splitext(os.path.basename(str(img)))[1]
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        elif len(img.shape) == 2:  # for gray inputs
+            img_mode = None
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        else:
+            img_mode = None
+
+        h, w = img.shape[0:2]
+        if h < 300:
+            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+
         if version == 'v1.2':
             face_enhancer = GFPGANer(
-                model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None)
+            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'v1.3':
             face_enhancer = GFPGANer(
-                model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None)
+            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'v1.4':
             face_enhancer = GFPGANer(
-                model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None)
+            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RestoreFormer':
+            face_enhancer = GFPGANer(
+            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'CodeFormer':
+             face_enhancer = GFPGANer(
+             model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'RealESR-General-x4v3':
-            face_enhancer = RealESRGANer(
-                scale=4, model_path='realesr-general-x4v3.pth', model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-        
-        if face_enhancer:
-            _, _, output = face_enhancer.enhance(input_img, has_aligned=False, only_center_face=False, paste_back=True)
-            
+             face_enhancer = GFPGANer(
+             model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
+
+        try:
+            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        except RuntimeError as error:
+            print('Error', error)
+
+        try:
             if scale != 2:
                 interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
-                h, w = input_img.shape[0:2]
+                h, w = img.shape[0:2]
                 output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
-                
-            output_path = f'output/out.jpg'
-            cv2.imwrite(output_path, output)
-            
-            return output_path
+        except Exception as error:
+            print('wrong scale input.', error)
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
         else:
-            return None
-    except Exception as e:
-        print(f"Error enhancing image: {e}")
-        return None
+            extension = 'jpg'
+        save_path = f'output/out.{extension}'
+        cv2.imwrite(save_path, output)
+
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return output, save_path
+    except Exception as error:
+        print('global exception', error)
+        return None, None
 
-# Download weights
-download_weights()
 
-# Initialize model
-model, half = initialize_models()
 
 
-@app.post("/process_image/")
-async def process_image(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)):
+@app.post("/upload/")
+async def upload_image(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)):
     try:
-        contents = await file.read()
-        img_path = "temp.jpg"
-        with open(img_path, "wb") as f:
-            f.write(contents)
-        
-        output_path = enhance_image(img_path, version, scale, model, half)
-        
-        if output_path:
-            return FileResponse(output_path, media_type='image/jpeg')
+        # Save the uploaded file
+        with open(f"uploaded_image{os.path.splitext(file.filename)[1]}", "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Perform image enhancement
+        enhanced_image, save_path = inference(f"uploaded_image{os.path.splitext(file.filename)[1]}", version, scale)
+
+        # Return the enhanced image
+        if enhanced_image is not None:
+            return FileResponse(path=save_path, media_type="image/jpeg")
         else:
-            return {"error": "Failed to process the image."}
+            return {"error": "Failed to enhance the image."}
     except Exception as e:
-        return {"error": f"An error occurred: {e}"}
+        return {"error": str(e)}
 
 app.mount("/", StaticFiles(directory="static", html=True), name="static")