Create app.py

app.py

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+import os
+os.sys.path
+
+!pip install opencv-python
+
+!pip install basicsr
+!pip install facexlib
+!pip install gfpgan
+!pip install tqdm
+!pip install -U gradio
+
+%pip install realesrgan
+
+import gradio as gr
+import cv2
+import numpy
+import os
+import random
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.download_util import load_file_from_url
+
+from realesrgan import RealESRGANer
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+last_file = None
+img_mode = "RGBA"
+
+
+def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
+    """Real-ESRGAN function to restore (and upscale) images.
+    """
+    if not img:
+        return
+
+    # Define model parameters
+    if model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
+    elif model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
+    elif model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
+    elif model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+        netscale = 2
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
+    elif model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = [
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
+        ]
+
+    # Determine model paths
+    model_path = os.path.join('weights', model_name + '.pth')
+    if not os.path.isfile(model_path):
+        ROOT_DIR = os.path.dirname(os.path.abspath("."))
+        for url in file_url:
+            # model_path will be updated
+            model_path = load_file_from_url(
+                url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
+
+    # Use dni to control the denoise strength
+    dni_weight = None
+    if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
+        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
+        model_path = [model_path, wdn_model_path]
+        dni_weight = [denoise_strength, 1 - denoise_strength]
+
+    # Restorer Class
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=dni_weight,
+        model=model,
+        tile=0,
+        tile_pad=10,
+        pre_pad=10,
+        half=False,
+        gpu_id=None
+    )
+
+    # Use GFPGAN for face enhancement
+    if face_enhance:
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
+            upscale=outscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)
+
+    # Convert the input PIL image to cv2 image, so that it can be processed by realesrgan
+    cv_img = numpy.array(img)
+    img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)
+
+    # Apply restoration
+    try:
+        if face_enhance:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        else:
+            output, _ = upsampler.enhance(img, outscale=outscale)
+    except RuntimeError as error:
+        print('Error', error)
+        print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+    else:
+        # Save restored image and return it to the output Image component
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            extension = 'jpg'
+
+        out_filename = f"output_{rnd_string(8)}.{extension}"
+        cv2.imwrite(out_filename, output)
+        global last_file
+        last_file = out_filename
+        return out_filename
+
+
+def rnd_string(x):
+    """Returns a string of 'x' random characters
+    """
+    characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
+    result = "".join((random.choice(characters)) for i in range(x))
+    return result
+
+
+def reset():
+    """Resets the Image components of the Gradio interface and deletes
+    the last processed image
+    """
+    global last_file
+    if last_file:
+        print(f"Deleting {last_file} ...")
+        os.remove(last_file)
+        last_file = None
+    return gr.update(value=None), gr.update(value=None)
+
+
+def has_transparency(img):
+    """This function works by first checking to see if a "transparency" property is defined
+    in the image's info -- if so, we return "True". Then, if the image is using indexed colors
+    (such as in GIFs), it gets the index of the transparent color in the palette
+    (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas
+    (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in
+    it, but it double-checks by getting the minimum and maximum values of every color channel
+    (img.getextrema()), and checks if the alpha channel's smallest value falls below 255.
+    https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
+    """
+    if img.info.get("transparency", None) is not None:
+        return True
+    if img.mode == "P":
+        transparent = img.info.get("transparency", -1)
+        for _, index in img.getcolors():
+            if index == transparent:
+                return True
+    elif img.mode == "RGBA":
+        extrema = img.getextrema()
+        if extrema[3][0] < 255:
+            return True
+    return False
+
+
+def image_properties(img):
+    """Returns the dimensions (width and height) and color mode of the input image and
+    also sets the global img_mode variable to be used by the realesrgan function
+    """
+    global img_mode
+    if img:
+        try:
+            img_mode = img.mode  # Get the color mode directly
+            properties = f"Width: {img.size[0]}, Height: {img.size[1]}  |  Color Mode: {img_mode}"
+            return properties
+        except Exception as e:
+            print(f"Error processing image: {e}")
+
+    return "Invalid image"
+
+def main():
+    # Gradio Interface
+    with gr.Blocks(title="PixelUP Demo", theme="dark") as demo:
+
+        gr.Markdown(
+            """# <div align="center">  </div>
+        <div align="center"><img width="200" height="74" src="https://i.imgur.com/UzFFDdL.png"></div>
+        """
+        )
+
+        with gr.Accordion("Options/Parameters"):
+            with gr.Row():
+                model_name = gr.Dropdown(label="Choose Model",
+                                         choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B",
+                                                  "RealESRGAN_x2plus", "realesr-general-x4v3"],
+                                         value="realesr-general-x4v3", show_label=True)
+                denoise_strength = gr.Slider(label="Denoise Strength",
+                                             minimum=0, maximum=1, step=0.1, value=0.5)
+                outscale = gr.Slider(label="Image Upscaling Factor",
+                                     minimum=1, maximum=10, step=1, value=2, show_label=True)
+                face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN ",
+                                           value=False, show_label=True)
+
+        with gr.Row():
+            with gr.Group():
+                input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA")
+                input_image_properties = gr.Textbox(label="Image Properties", max_lines=1)
+            output_image = gr.Image(label="Restored Image", image_mode="RGBA")
+        with gr.Row():
+            restore_btn = gr.Button("Restore Image")
+            reset_btn = gr.Button("Reset")
+
+        # Event listeners:
+        input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties)
+        restore_btn.click(fn=realesrgan,
+                          inputs=[input_image, model_name, denoise_strength, face_enhance, outscale],
+                          outputs=output_image)
+        reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image])
+        # reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n")
+        # Undocumented method to clear a component's value using Javascript
+
+        gr.Markdown(
+            """*Please note that support for animated GIFs is not yet implemented. Should an animated GIF is chosen for restoration,
+            the demo will output only the first frame saved in PNG format (to preserve probable transparency).*
+        """
+        )
+
+    demo.launch(share=True)
+
+
+if __name__ == "__main__":
+    main()