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README.md

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----
-title: Background Remover
-emoji: 😻
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 5.46.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Remove Video Background
+emoji: 🎞️
+colorFrom: purple
+colorTo: gray
+sdk: gradio
+sdk_version: 5.1.0
+app_file: app.py
+pinned: true
+short_description: Easily remove your videos background!
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import time
+import random
+from PIL import Image
+import torch
+import re
+
+torch.jit.script = lambda f: f
+
+from transparent_background import Remover
+
+def doo(video, color, mode, progress=gr.Progress()):
+    print(str(color))
+    if str(color).startswith('#'):
+        color = color.lstrip('#')
+        rgb = tuple(int(color[i:i+2], 16) for i in (0, 2, 4))
+        color = str(list(rgb))
+    elif str(color).startswith('rgba'):
+        rgba_match = re.match(r'rgba\(([\d.]+), ([\d.]+), ([\d.]+), [\d.]+\)', color)
+        if rgba_match:
+            r, g, b = rgba_match.groups()  # Extract r, g, b values
+            color = str([int(float(r)), int(float(g)), int(float(b))])
+    print(color)
+    if mode == 'Fast':
+        remover = Remover(mode='fast')
+    else:
+        remover = Remover()
+
+    cap = cv2.VideoCapture(video)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get total frames
+    writer = None
+    tmpname = random.randint(111111111, 999999999)
+    processed_frames = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+
+        if ret is False:
+            break
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(frame).convert('RGB')
+
+        if writer is None:
+            writer = cv2.VideoWriter(str(tmpname) + '.mp4', cv2.VideoWriter_fourcc(*'mp4v'), cap.get(cv2.CAP_PROP_FPS), img.size)
+
+        processed_frames += 1
+        print(f"Processing frame {processed_frames}")
+        progress(processed_frames / total_frames, desc=f"Processing frame {processed_frames}/{total_frames}")
+        out = remover.process(img, type=color)
+        writer.write(cv2.cvtColor(np.array(out), cv2.COLOR_BGR2RGB))
+
+    cap.release()
+    writer.release()
+    return str(tmpname) + '.mp4'
+
+title = "🎞️ Video Background Removal Tool 🎥"
+description = """*Please note that if your video file is long (has a high number of frames), there is a chance that processing break due to GPU timeout. In this case, consider trying Fast mode."""
+
+examples = [['./input.mp4']]
+
+iface = gr.Interface(
+    fn=doo,
+    inputs=["video", gr.ColorPicker(label="Background color", value="#00FF00"), gr.components.Radio(['Normal', 'Fast'], label='Select mode', value='Normal', info='Normal is more accurate, but takes longer. | Fast has lower accuracy so the process will be faster.')],
+    outputs="video",
+    examples=examples,
+    title=title,
+    description=description
+)
+iface.launch()

image_resizer.py

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+import gradio as gr
+from PIL import Image, ImageOps
+import numpy as np
+import cv2
+
+
+def get_new_size_and_padding(old_w, old_h, target_ratio):
+    old_ratio = old_w / old_h
+    if old_ratio > target_ratio:
+        # Image is wider than target: pad height
+        new_w = old_w
+        new_h = int(old_w / target_ratio)
+        pad_top = (new_h - old_h) // 2
+        pad_bottom = new_h - old_h - pad_top
+        pad_left = pad_right = 0
+    else:
+        # Image is taller than target: pad width
+        new_h = old_h
+        new_w = int(old_h * target_ratio)
+        pad_left = (new_w - old_w) // 2
+        pad_right = new_w - old_w - pad_left
+        pad_top = pad_bottom = 0
+    return (new_w, new_h), (pad_left, pad_top, pad_right, pad_bottom)
+
+
+def image_filler(img, padding):
+    # Use OpenCV to blur the border region for a simple "image filler"
+    np_img = np.array(img)
+    h, w = np_img.shape[:2]
+    pad_left, pad_top, pad_right, pad_bottom = padding
+
+    # Create a blurred version of the image
+    blurred = cv2.GaussianBlur(np_img, (51, 51), 0)
+
+    # Create a new image with the blurred background
+    new_h = h + pad_top + pad_bottom
+    new_w = w + pad_left + pad_right
+    result = np.zeros((new_h, new_w, 3), dtype=np.uint8)
+    result[:, :] = blurred[0, 0]  # Fill with a color from the image
+
+    # Place the blurred image in the background
+    result[:, :] = blurred[0, 0]
+    result[pad_top : pad_top + h, pad_left : pad_left + w] = np_img
+
+    # Blend the edges for a smooth transition
+    # (For simplicity, just use the blurred background)
+    return Image.fromarray(result)
+
+
+def resize_with_border(image, aspect_ratio, border_fill):
+    # Parse aspect ratio
+    w_ratio, h_ratio = map(int, aspect_ratio.split(":"))
+    target_ratio = w_ratio / h_ratio
+
+    # Ensure image is RGB
+    image = image.convert("RGB")
+    old_w, old_h = image.size
+
+    (new_w, new_h), padding = get_new_size_and_padding(old_w, old_h, target_ratio)
+
+    if border_fill == "White":
+        # Use Pillow's expand with white fill
+        result = ImageOps.expand(image, border=padding, fill=(255, 255, 255))
+    else:
+        # Use image filler (blurred border)
+        result = image_filler(image, padding)
+
+    # Resize to exact target size (optional, in case of rounding)
+    result = result.resize((new_w, new_h), Image.LANCZOS)
+    return result
+
+
+aspect_ratios = ["1:1", "4:3", "16:9", "3:2", "21:9"]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Resizer with Border Fill")
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Image")
+            aspect_input = gr.Dropdown(
+                aspect_ratios, value="1:1", label="Target Aspect Ratio"
+            )
+            fill_input = gr.Radio(
+                ["White", "Image Filler"], value="White", label="Border Fill"
+            )
+            submit_btn = gr.Button("Resize Image")
+        with gr.Column():
+            image_output = gr.Image(type="pil", label="Resized Image")
+    submit_btn.click(
+        resize_with_border,
+        inputs=[image_input, aspect_input, fill_input],
+        outputs=image_output,
+    )
+
+demo.launch()

input.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b83e1d4b264e50e945abf630cca5c6d0e387d84435a14fa1ca679a6a50b4608
+size 266619

requirements.txt

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+torch
+torchvision
+opencv-python
+timm
+tqdm
+kornia
+gdown
+transparent-background

temp.py

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+import spaces
+import gradio as gr
+import cv2
+import numpy as np
+import time
+import random
+from PIL import Image
+import torch
+import re
+
+torch.jit.script = lambda f: f
+
+from transparent_background import Remover
+
+@spaces.GPU(duration=90)
+def doo(video, color, mode, progress=gr.Progress()):
+    print(str(color))
+    if str(color).startswith('#'):
+        color = color.lstrip('#')
+        rgb = tuple(int(color[i:i+2], 16) for i in (0, 2, 4))
+        color = str(list(rgb))
+    elif str(color).startswith('rgba'):
+        rgba_match = re.match(r'rgba\(([\d.]+), ([\d.]+), ([\d.]+), [\d.]+\)', color)
+        if rgba_match:
+            r, g, b = rgba_match.groups()  # Extract r, g, b values
+            color = str([int(float(r)), int(float(g)), int(float(b))])
+    print(color)
+    if mode == 'Fast':
+        remover = Remover(mode='fast')
+    else:
+        remover = Remover()
+
+    cap = cv2.VideoCapture(video)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get total frames
+    writer = None
+    tmpname = random.randint(111111111, 999999999)
+    processed_frames = 0
+    start_time = time.time()
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+
+        if ret is False:
+            break
+
+        if time.time() - start_time >= 20 * 60 - 5:
+            print("GPU Timeout is coming")
+            cap.release()
+            writer.release()
+            return str(tmpname) + '.mp4'
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(frame).convert('RGB')
+
+        if writer is None:
+            writer = cv2.VideoWriter(str(tmpname) + '.mp4', cv2.VideoWriter_fourcc(*'mp4v'), cap.get(cv2.CAP_PROP_FPS), img.size)
+
+        processed_frames += 1
+        print(f"Processing frame {processed_frames}")
+        progress(processed_frames / total_frames, desc=f"Processing frame {processed_frames}/{total_frames}")
+        out = remover.process(img, type="overlay")
+        writer.write(cv2.cvtColor(np.array(out), cv2.COLOR_BGR2RGB))
+
+    cap.release()
+    writer.release()
+    return str(tmpname) + '.mp4'
+
+title = "🎞️ Video Background Removal Tool 🎥"
+description = """*Please note that if your video file is long (has a high number of frames), there is a chance that processing break due to GPU timeout. In this case, consider trying Fast mode."""
+
+examples = [['./input.mp4']]
+
+iface = gr.Interface(
+    fn=doo,
+    inputs=["video", gr.ColorPicker(label="Background color", value="#00FF00"), gr.components.Radio(['Normal', 'Fast'], label='Select mode', value='Normal', info='Normal is more accurate, but takes longer. | Fast has lower accuracy so the process will be faster.')],
+    outputs="video",
+    examples=examples,
+    title=title,
+    description=description
+)
+iface.launch(share=True)