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from torch import nn |
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import numpy as np |
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from PIL import Image |
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import gradio as gr |
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from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation |
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def ade_palette(): |
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"""ADE20K palette that maps each class to RGB values.""" |
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return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], |
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[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], |
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[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], |
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[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], |
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[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], |
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[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], |
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[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], |
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[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], |
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[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], |
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[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], |
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[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], |
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[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], |
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[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], |
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[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], |
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[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], |
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[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], |
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[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], |
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[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], |
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[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], |
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[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], |
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[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], |
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[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], |
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[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], |
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[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], |
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[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], |
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[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], |
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[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], |
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[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], |
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[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], |
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[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], |
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[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], |
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[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], |
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[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], |
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[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], |
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[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], |
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[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], |
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[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], |
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[102, 255, 0], [92, 0, 255]] |
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def resize_image(image, new_size, sdxl_resize=None): |
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""" |
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Resizes the given image while maintaining its aspect ratio. |
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Args: |
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image (PIL.Image): The image to be resized. |
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new_size (int): The new size (width or height) to resize the image to. |
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sdxl_resize (bool, optional): Flag indicating whether to resize based on \ |
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the larger dimension. Default is None. |
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Returns: |
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PIL.Image: The resized image. |
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""" |
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original_width, original_height = image.size |
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if sdxl_resize: |
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value = max(original_height, original_width) |
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else: |
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value = min(original_height, original_width) |
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if value == original_height: |
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aspect_ratio = original_width / original_height |
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new_height = new_size |
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new_width = int(new_height * aspect_ratio) |
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else: |
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aspect_ratio = original_height / original_width |
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new_width = new_size |
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new_height = int(new_width * aspect_ratio) |
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resized_image = image.resize((new_width, new_height)) |
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w, h = resized_image.size |
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w, h = map(lambda x: x - x % 64, (w, h)) |
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resized_image = resized_image.resize((w, h)) |
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return resized_image |
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def run(img): |
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extractor = AutoFeatureExtractor.from_pretrained("mohit-mahavar/segformer-b0-finetuned-segments-sidewalk-july-24") |
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model = SegformerForSemanticSegmentation.from_pretrained("mohit-mahavar/segformer-b0-finetuned-segments-sidewalk-july-24") |
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if min(img.size) >= 768: |
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img = resize_image(img, 768) |
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elif max(img.size) >= 1024: |
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img = resize_image(img, 1024, sdxl_resize=True) |
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elif min(img.size) >= 512: |
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img = resize_image(img, 512) |
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elif max(img.size) >= 768: |
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img = resize_image(img, 768, sdxl_resize=True) |
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elif max(img.size) >= 512: |
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img = resize_image(img, 512, sdxl_resize=True) |
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pixel_values = extractor(img, return_tensors="pt").pixel_values.to("cpu") |
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outputs = model(pixel_values) |
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logits = outputs.logits |
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logits = nn.functional.interpolate(outputs.logits.detach().cpu(), |
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size=img.size[::-1], |
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mode='bilinear', |
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align_corners=False) |
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seg = logits.argmax(dim=1)[0] |
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) |
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palette = np.array(ade_palette()) |
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for label, color in enumerate(palette): |
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color_seg[seg == label, :] = color |
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color_seg = color_seg[..., ::-1] |
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img = np.array(img) * 0.5 + color_seg * 0.5 |
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img = img.astype(np.uint8) |
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img = Image.fromarray(img) |
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return img |
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iface = gr.Interface( |
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fn=run, |
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inputs=gr.Image(label="Input image", type="pil"), |
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examples=["1.jpg" , "2.jpg", "3.jpg" , "4.jpg", "5.jpg" , "6.jpg"], |
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outputs=gr.Image(label="Output image with predicted instance Masks", type="pil"), |
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title="Image Segmentation with Segments-Sidewalk-SegFormer-B0", |
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description="Upload an image, and this app will perform image segmentation and display the result", |
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) |
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iface.launch(debug=True) |
