Upload app (25).py

app (25).py

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+import os
+import cv2
+import gradio as gr
+import torch
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+from gfpgan.utils import GFPGANer
+from realesrgan.utils import RealESRGANer
+from zeroscratches import EraseScratches
+
+os.system("pip freeze")
+
+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 enhance_image(img, version, scale):
+    # weight /= 100
+    print(img, version, scale)
+    try:
+        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 == 'M1':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'M2':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'M3':
+            face_enhancer = GFPGANer(
+            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 = 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 = img.shape[0:2]
+                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+        except Exception as error:
+            print('wrong scale input.', error)
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            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
+
+# Function to remove scratches from an image
+def remove_scratches(img):
+    scratch_remover = EraseScratches()
+    img_without_scratches = scratch_remover.erase(img)
+    return img_without_scratches
+
+
+
+import tempfile
+
+# Function for performing operations sequentially
+def process_image(img):
+    try:
+        # Create a unique temporary directory for each request
+        temp_dir = tempfile.mkdtemp()
+
+        # Generate a unique filename for the temporary file
+        unique_filename = 'temp_image.jpg'
+        temp_file_path = os.path.join(temp_dir, unique_filename)
+
+        # Remove scratches from the input image
+        img_without_scratches = remove_scratches(img)
+
+        # Save the image without scratches to the temporary file
+        cv2.imwrite(temp_file_path, cv2.cvtColor(img_without_scratches, cv2.COLOR_BGR2RGB))
+
+        # Enhance the image using the saved file path
+        enhanced_img, save_path = enhance_image(temp_file_path, version='M2', scale=2)
+
+        # Convert the enhanced image to RGB format
+        enhanced_img_rgb = cv2.cvtColor(enhanced_img, cv2.COLOR_BGR2RGB)
+
+        # Delete the temporary file and directory
+        os.remove(temp_file_path)
+        os.rmdir(temp_dir)
+
+        # Return the enhanced image in RGB format and the path where it's saved
+        return enhanced_img, save_path
+    except Exception as e:
+        print('Error processing image:', e)
+        return None, None
+
+# Gradio interface
+title = "<span style='color: crimson;'>Aiconvert.online</span>"
+description = r"""
+"""
+article = r"""
+
+"""
+demo = gr.Interface(
+    process_image, [
+        gr.Image(type="pil", label="Input"),
+    ], [
+        gr.Image(type="numpy", label="Result Image"), 
+        gr.File(label="Download the output image")
+    ],
+    title=title,
+    description=description,
+    article=article)
+
+demo.queue().launch()
+
+