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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 |
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MODEL_ID = "mattmdjaga/segformer_b2_clothes" |
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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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[156, 200, 56],[32, 90, 210],[56, 123, 67],[180, 56, 123],[123, 67, 45],[45, 134, 200], |
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[67, 56, 123],[78, 123, 67],[32, 210, 90],[45, 56, 189],[123, 56, 123],[56, 156, 200], |
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[189, 56, 45],[112, 200, 56],[56, 123, 45],[200, 32, 90],[123, 45, 78],[200, 156, 56], |
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[45, 67, 123],[56, 45, 78],[45, 56, 123],[123, 67, 56],[56, 78, 123],[210, 90, 32], |
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[123, 56, 189],[45, 200, 134],[67, 123, 56],[123, 45, 67],[90, 32, 210],[200, 45, 78], |
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[32, 210, 90],[45, 123, 67],[165, 42, 87],[72, 145, 167],[15, 158, 75],[209, 89, 40], |
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[32, 21, 121],[184, 20, 100],[56, 135, 15],[128, 92, 176],[1, 119, 140],[220, 151, 43], |
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[41, 97, 72],[148, 38, 27],[107, 86, 176],[21, 26, 136],[174, 27, 90],[91, 96, 204], |
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[108, 50, 107],[27, 45, 136],[168, 200, 52],[7, 102, 27],[42, 93, 56],[140, 52, 112], |
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[92, 107, 168],[17, 118, 176],[59, 50, 174],[206, 40, 143],[44, 19, 142],[23, 168, 75], |
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[54, 57, 189],[144, 21, 15],[15, 176, 35],[107, 19, 79],[204, 52, 114],[48, 173, 83], |
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[11, 120, 53],[206, 104, 28],[20, 31, 153],[27, 21, 93],[11, 206, 138],[112, 30, 83], |
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[68, 91, 152],[153, 13, 43],[25, 114, 54],[92, 27, 150],[108, 42, 59],[194, 77, 5], |
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[145, 48, 83],[7, 113, 19],[25, 92, 113],[60, 168, 79],[78, 33, 120],[89, 176, 205], |
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[27, 200, 94],[210, 67, 23],[123, 89, 189],[225, 56, 112],[75, 156, 45],[172, 104, 200], |
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[15, 170, 197],[240, 133, 65],[89, 156, 112],[214, 88, 57],[156, 134, 200],[78, 57, 189], |
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[200, 78, 123],[106, 120, 210],[145, 56, 112],[89, 120, 189],[185, 206, 56],[47, 99, 28], |
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[112, 189, 78],[200, 112, 89],[89, 145, 112],[78, 106, 189],[112, 78, 189],[156, 112, 78], |
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[28, 210, 99],[78, 89, 189],[189, 78, 57],[112, 200, 78],[189, 47, 78],[205, 112, 57], |
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[78, 145, 57],[200, 78, 112],[99, 89, 145],[200, 156, 78],[57, 78, 145],[78, 57, 99], |
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[57, 78, 145],[145, 112, 78],[78, 89, 145],[210, 99, 28],[145, 78, 189],[57, 200, 136], |
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[89, 156, 78],[145, 78, 99],[99, 28, 210],[189, 78, 47],[28, 210, 99],[78, 145, 57], |
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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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fig = draw_plot(pred_img, seg) |
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return fig |
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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=gr.Plot(label="Overlay + Legend"), |
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examples=[ |
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"person-1.jpg", |
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"person-2.jpg", |
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"person-3.jpg", |
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"person-4.jpg", |
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"person-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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