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import gradio as gr |
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from matplotlib import gridspec |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from PIL import Image |
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import torch |
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from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation, pipeline |
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MODEL_ID = "nvidia/segformer-b0-finetuned-cityscapes-768-768" |
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processor = AutoImageProcessor.from_pretrained(MODEL_ID) |
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model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID) |
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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 [ |
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[204, 87, 92],[112, 185, 212],[45, 189, 106],[234, 123, 67],[78, 56, 123],[210, 32, 89], |
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[90, 180, 56],[155, 102, 200],[33, 147, 176],[255, 183, 76],[67, 123, 89],[190, 60, 45], |
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[134, 112, 200],[56, 45, 189],[200, 56, 123],[87, 92, 204],[120, 56, 123],[45, 78, 123], |
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[123, 123, 123] |
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] |
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labels_list = [] |
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with open("labels.txt", "r", encoding="utf-8") as fp: |
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for line in fp: |
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labels_list.append(line.rstrip("\n")) |
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colormap = np.asarray(ade_palette(), dtype=np.uint8) |
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def label_to_color_image(label): |
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if label.ndim != 2: |
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raise ValueError("Expect 2-D input label") |
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if np.max(label) >= len(colormap): |
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raise ValueError("label value too large.") |
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return colormap[label] |
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def draw_plot(pred_img, seg_np): |
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fig = plt.figure(figsize=(20, 15)) |
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grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1]) |
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plt.subplot(grid_spec[0]) |
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plt.imshow(pred_img) |
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plt.axis('off') |
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LABEL_NAMES = np.asarray(labels_list) |
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FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1) |
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FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP) |
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unique_labels = np.unique(seg_np.astype("uint8")) |
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ax = plt.subplot(grid_spec[1]) |
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plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest") |
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ax.yaxis.tick_right() |
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plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels]) |
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plt.xticks([], []) |
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ax.tick_params(width=0.0, labelsize=25) |
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return fig |
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def run_inference(input_img): |
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img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img |
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if img.mode != "RGB": |
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img = img.convert("RGB") |
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inputs = processor(images=img, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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upsampled = torch.nn.functional.interpolate( |
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logits, size=img.size[::-1], mode="bilinear", align_corners=False |
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) |
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seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8) |
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color_seg = colormap[seg] |
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pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8) |
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total_pixels = seg.size |
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unique_labels, counts = np.unique(seg, return_counts=True) |
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label_results = {} |
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for label_id, count in zip(unique_labels, counts): |
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class_name = labels_list[label_id] |
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percentage = count / total_pixels |
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label_results[class_name] = percentage |
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fig = draw_plot(pred_img, seg) |
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return fig, label_results |
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demo = gr.Interface( |
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fn=run_inference, |
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inputs=gr.Image(type="numpy", label="Input Image"), |
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outputs=[ |
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gr.Plot(label="Overlay + Legend"), |
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gr.Label(label="Class Proportions", num_top_classes=5) |
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], |
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examples=[ |
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"ADE_val_00000001.jpeg", |
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"ADE_val_00001248.jpg", |
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"cityscapes_1.jpg", |
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"cityscapes_2.jpg", |
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"cityscapes_3.jpg", |
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"cityscapes_4.jpg", |
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"cityscapes_5.jpg" |
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], |
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flagging_mode="never", |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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