Update app.py

app.py

@@ -1,151 +1,151 @@
-import os
-import gradio as gr
-from gradio_imageslider import ImageSlider
-from loadimg import load_img
-import spaces
-from transformers import AutoModelForImageSegmentation
-import torch
-from torchvision import transforms
-from PIL import Image, ImageChops
-from moviepy.editor import VideoFileClip, ImageSequenceClip
-import numpy as np
-from tqdm import tqdm
-from uuid import uuid1
-
-# Check CUDA availability
-if torch.cuda.is_available():
-    device = "cuda"
-else:
-    device = "cpu"
-
-torch.set_float32_matmul_precision(["high", "highest"][0])
-
-# Load the model
-birefnet = AutoModelForImageSegmentation.from_pretrained(
-    "briaai/RMBG-2.0", trust_remote_code=True
-)
-birefnet.to(device)
-transform_image = transforms.Compose(
-    [
-        transforms.Resize((1024, 1024)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ]
-)
-
-output_folder = 'output_images'
-if not os.path.exists(output_folder):
-    os.makedirs(output_folder)
-
-def fn(image):
-    im = load_img(image, output_type="pil")
-    im = im.convert("RGB")
-    origin = im.copy()
-    image = process(im)
-    image_path = os.path.join(output_folder, "no_bg_image.png")
-    image.save(image_path)
-    return (image, origin), image_path
-
-@spaces.GPU
-def process(image):
-    image_size = image.size
-    input_images = transform_image(image).unsqueeze(0).to(device)
-    # Prediction
-    with torch.no_grad():
-        preds = birefnet(input_images)[-1].sigmoid().cpu()
-    pred = preds[0].squeeze()
-    pred_pil = transforms.ToPILImage()(pred)
-    mask = pred_pil.resize(image_size)
-    image.putalpha(mask)
-    return image
-
-def process_file(f):
-    name_path = f.rsplit(".",1)[0]+".png"
-    im = load_img(f, output_type="pil")
-    im = im.convert("RGB")
-    transparent = process(im)
-    transparent.save(name_path)
-    return name_path
-
-def remove_background(image):
-    """Remove background from a single image."""
-    input_images = transform_image(image).unsqueeze(0).to(device)
-
-    # Prediction
-    with torch.no_grad():
-        preds = birefnet(input_images)[-1].sigmoid().cpu()
-    pred = preds[0].squeeze()
-
-    # Convert the prediction to a mask
-    mask = (pred * 255).byte()  # Convert to 0-255 range
-    mask_pil = transforms.ToPILImage()(mask).convert("L")
-    mask_resized = mask_pil.resize(image.size, Image.LANCZOS)
-
-    # Apply the mask to the image
-    image.putalpha(mask_resized)
-
-    return image, mask_resized
-
-def process_video(input_video_path):
-    """Process a video to remove the background from each frame."""
-    # Load the video
-    video_clip = VideoFileClip(input_video_path)
-
-    # Process each frame
-    frames = []
-    for frame in tqdm(video_clip.iter_frames()):
-        frame_pil = Image.fromarray(frame)
-        frame_no_bg, mask_resized = remove_background(frame_pil)
-        path = "{}.png".format(uuid1())
-        frame_no_bg.save(path)
-        frame_no_bg = Image.open(path).convert("RGBA")
-        os.remove(path)
-
-        # Convert mask_resized to RGBA mode
-        mask_resized_rgba = mask_resized.convert("RGBA")
-
-        # Apply the mask using ImageChops.multiply
-        output = ImageChops.multiply(frame_no_bg, mask_resized_rgba)
-        output_np = np.array(output)
-        frames.append(output_np)
-
-    # Save the processed frames as a new video
-    output_video_path = os.path.join(output_folder, "no_bg_video.mp4")
-    processed_clip = ImageSequenceClip(frames, fps=video_clip.fps)
-    processed_clip.write_videofile(output_video_path, codec='libx264', ffmpeg_params=['-pix_fmt', 'yuva420p'])
-
-    return output_video_path
-
-# Gradio components
-slider1 = ImageSlider(label="RMBG-2.0", type="pil")
-slider2 = ImageSlider(label="RMBG-2.0", type="pil")
-image = gr.Image(label="Upload an image")
-image2 = gr.Image(label="Upload an image", type="filepath")
-text = gr.Textbox(label="Paste an image URL")
-png_file = gr.File(label="output png file")
-video_input = gr.Video(label="Upload a video")
-video_output = gr.Video(label="Processed video")
-
-# Example videos
-example_videos = [
-    "pexels-cottonbro-5319934.mp4",
-    "300_A_car_is_running_on_the_road.mp4",
-    "A_Terracotta_Warrior_is_skateboarding_9033688.mp4"
-]
-
-# Gradio interfaces
-tab1 = gr.Interface(
-    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[load_img("giraffe.jpg", output_type="pil")], api_name="image"
-)
-
-tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=["http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg"], api_name="text")
-#tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png")
-tab4 = gr.Interface(process_video, inputs=video_input, outputs=video_output, examples=example_videos, api_name="video")
-
-# Gradio tabbed interface
-demo = gr.TabbedInterface(
-    [tab4, tab1, tab2], ["input video", "input image", "input url"], title="RMBG-2.0 for background removal"
-)
-
-if __name__ == "__main__":
-    demo.launch(share=True, show_error=True)
+import os
+import gradio as gr
+from gradio_imageslider import ImageSlider
+from loadimg import load_img
+import spaces
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+from PIL import Image, ImageChops
+from moviepy.editor import VideoFileClip, ImageSequenceClip
+import numpy as np
+from tqdm import tqdm
+from uuid import uuid1
+
+# Check CUDA availability
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+torch.set_float32_matmul_precision(["high", "highest"][0])
+
+# Load the model
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "briaai/RMBG-2.0", trust_remote_code=True
+)
+birefnet.to(device)
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+output_folder = 'output_images'
+if not os.path.exists(output_folder):
+    os.makedirs(output_folder)
+
+def fn(image):
+    im = load_img(image, output_type="pil")
+    im = im.convert("RGB")
+    origin = im.copy()
+    image = process(im)
+    image_path = os.path.join(output_folder, "no_bg_image.png")
+    image.save(image_path)
+    return (image, origin), image_path
+
+@spaces.GPU
+def process(image):
+    image_size = image.size
+    input_images = transform_image(image).unsqueeze(0).to(device)
+    # Prediction
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image_size)
+    image.putalpha(mask)
+    return image
+
+def process_file(f):
+    name_path = f.rsplit(".",1)[0]+".png"
+    im = load_img(f, output_type="pil")
+    im = im.convert("RGB")
+    transparent = process(im)
+    transparent.save(name_path)
+    return name_path
+
+def remove_background(image):
+    """Remove background from a single image."""
+    input_images = transform_image(image).unsqueeze(0).to(device)
+
+    # Prediction
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+
+    # Convert the prediction to a mask
+    mask = (pred * 255).byte()  # Convert to 0-255 range
+    mask_pil = transforms.ToPILImage()(mask).convert("L")
+    mask_resized = mask_pil.resize(image.size, Image.LANCZOS)
+
+    # Apply the mask to the image
+    image.putalpha(mask_resized)
+
+    return image, mask_resized
+
+def process_video(input_video_path):
+    """Process a video to remove the background from each frame."""
+    # Load the video
+    video_clip = VideoFileClip(input_video_path)
+
+    # Process each frame
+    frames = []
+    for frame in tqdm(video_clip.iter_frames()):
+        frame_pil = Image.fromarray(frame)
+        frame_no_bg, mask_resized = remove_background(frame_pil)
+        path = "{}.png".format(uuid1())
+        frame_no_bg.save(path)
+        frame_no_bg = Image.open(path).convert("RGBA")
+        os.remove(path)
+
+        # Convert mask_resized to RGBA mode
+        mask_resized_rgba = mask_resized.convert("RGBA")
+
+        # Apply the mask using ImageChops.multiply
+        output = ImageChops.multiply(frame_no_bg, mask_resized_rgba)
+        output_np = np.array(output)
+        frames.append(output_np)
+
+    # Save the processed frames as a new video
+    output_video_path = os.path.join(output_folder, "no_bg_video.mp4")
+    processed_clip = ImageSequenceClip(frames, fps=video_clip.fps)
+    processed_clip.write_videofile(output_video_path, codec='libx264', ffmpeg_params=['-pix_fmt', 'yuva420p'])
+
+    return output_video_path
+
+# Gradio components
+slider1 = ImageSlider(label="RMBG-2.0", type="pil")
+slider2 = ImageSlider(label="RMBG-2.0", type="pil")
+image = gr.Image(label="Upload an image")
+image2 = gr.Image(label="Upload an image", type="filepath")
+text = gr.Textbox(label="Paste an image URL")
+png_file = gr.File(label="output png file")
+video_input = gr.Video(label="Upload a video")
+video_output = gr.Video(label="Processed video")
+
+# Example videos
+example_videos = [
+    "pexels-cottonbro-5319934.mp4",
+    "300_A_car_is_running_on_the_road.mp4",
+    "A_Terracotta_Warrior_is_skateboarding_9033688.mp4"
+]
+
+# Gradio interfaces
+tab1 = gr.Interface(
+    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[load_img("giraffe.jpg", output_type="pil")], api_name="image"
+)
+
+tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=["http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg"], api_name="text")
+#tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png")
+tab4 = gr.Interface(process_video, inputs=video_input, outputs=video_output, examples=example_videos, api_name="video", cache_examples = False)
+
+# Gradio tabbed interface
+demo = gr.TabbedInterface(
+    [tab4, tab1, tab2], ["input video", "input image", "input url"], title="RMBG-2.0 for background removal"
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True, show_error=True)