Create 05_image_Denoiser

pages/05_image_Denoiser

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+import streamlit as st
+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 PIL import Image
+
+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(__file__))
+        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.get_value(), dtype = 'uint8')
+    cv_img = numpy.array(img)
+    #img = cv2.cvtColor(cv2.UMat(imgUMat), cv2.COLOR_RGB2GRAY)
+    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:
+        if has_transparency(img):
+            img_mode = "RGBA"
+        else:
+            img_mode = "RGB"
+        properties = f"Width: {img.size[0]}, Height: {img.size[1]}  |  Color Mode: {img_mode}"
+        return properties
+    
+def image_properties(image):
+    # Function to display image properties
+    properties = f"Image Size: {image.size}\nImage Mode: {image.mode}"
+    return properties
+
+#----------
+
+input_folder = '.'
+
+@st.cache_resource
+def load_image(image_file):
+  img = Image.open(image_file)
+  return img
+
+def save_image(image_file):
+  if image_file is not None:
+    filename = image_file.name
+    img = load_image(image_file)
+    st.image(image=img, width=None)
+    with open(os.path.join(input_folder, filename), "wb") as f:
+      f.write(image_file.getbuffer())
+      st.success("Succesfully uploaded file for processing".format(filename))
+
+#------------
+
+st.title("Image Denoiser")
+# Saving uploaded image in input folder for processing
+
+#with st.expander("Options/Parameters"):   
+
+input_img = st.file_uploader(
+"Upload Image", type=['png', 'jpeg', 'jpg', 'webp'])
+#save_image(input_img)
+
+model_name = "realesr-general-x4v3"
+
+denoise_strength = st.slider("Denoise Strength", 0.0, 1.0, 0.5)
+
+outscale = 1
+
+face_enhance = False
+
+if input_img:
+  print(input_img)
+  input_img = Image.open(input_img)
+  # Display image properties
+  cols = st.columns(2)
+
+  cols[0].image(input_img, 'Source Image')
+
+  #input_properties = get_image_properties(input_img)
+  #cols[1].write(input_properties)
+
+  # Output placeholder
+  output_img = st.empty()
+
+# Input and output placeholders
+input_img = input_img
+output_img = st.empty()
+
+# Buttons
+restore = st.button('Restore')
+reset = st.button('Reset')
+
+# Restore clicked
+if restore:
+    if input_img is not None:
+        output = realesrgan(input_img, model_name, denoise_strength, 
+                            face_enhance, outscale)
+        output_img.image(output, 'Restored Image')
+    else:
+        st.warning('Upload a file', icon="⚠️")
+
+# Reset clicked    
+if reset:
+    output_img.empty()
+