| import gradio as gr |
| from matplotlib import gridspec |
| import matplotlib.pyplot as plt |
| import numpy as np |
| from PIL import Image |
| import torch |
| from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation |
|
|
| MODEL_ID = "mattmdjaga/segformer_b2_clothes" |
| processor = AutoImageProcessor.from_pretrained(MODEL_ID) |
| model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID) |
|
|
| def ade_palette(): |
| """ADE20K palette that maps each class to RGB values.""" |
| return [ |
| [204, 87, 92],[112, 185, 212],[45, 189, 106],[234, 123, 67],[78, 56, 123],[210, 32, 89], |
| [90, 180, 56],[155, 102, 200],[33, 147, 176],[255, 183, 76],[67, 123, 89],[190, 60, 45], |
| [134, 112, 200],[56, 45, 189],[200, 56, 123],[87, 92, 204],[120, 56, 123],[45, 78, 123] |
| ] |
|
|
| labels_list = [] |
| with open("labels.txt", "r", encoding="utf-8") as fp: |
| for line in fp: |
| labels_list.append(line.rstrip("\n")) |
|
|
| colormap = np.asarray(ade_palette(), dtype=np.uint8) |
|
|
| def label_to_color_image(label): |
| if label.ndim != 2: |
| raise ValueError("Expect 2-D input label") |
| if np.max(label) >= len(colormap): |
| raise ValueError("label value too large.") |
| return colormap[label] |
|
|
| def draw_plot(pred_img, seg_np): |
| fig = plt.figure(figsize=(20, 15)) |
| grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1]) |
|
|
| plt.subplot(grid_spec[0]) |
| plt.imshow(pred_img) |
| plt.axis('off') |
|
|
| LABEL_NAMES = np.asarray(labels_list) |
| FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1) |
| FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP) |
|
|
| unique_labels = np.unique(seg_np.astype("uint8")) |
| ax = plt.subplot(grid_spec[1]) |
| plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest") |
| ax.yaxis.tick_right() |
| plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels]) |
| plt.xticks([], []) |
| ax.tick_params(width=0.0, labelsize=25) |
| return fig |
|
|
| def run_inference(input_img): |
| |
| img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img |
| if img.mode != "RGB": |
| img = img.convert("RGB") |
|
|
| inputs = processor(images=img, return_tensors="pt") |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| logits = outputs.logits |
|
|
| |
| upsampled = torch.nn.functional.interpolate( |
| logits, size=img.size[::-1], mode="bilinear", align_corners=False |
| ) |
| seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8) |
|
|
| |
| color_seg = colormap[seg] |
| pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8) |
|
|
| fig = draw_plot(pred_img, seg) |
| return fig |
|
|
| demo = gr.Interface( |
| fn=run_inference, |
| inputs=gr.Image(type="numpy", label="Input Image"), |
| outputs=gr.Plot(label="Overlay + Legend"), |
| examples=[ |
| "person-1.jpg", |
| "person-2.jpg", |
| "person-3.jpg", |
| "person-4.jpg", |
| "person-5.jpg" |
| ], |
| flagging_mode="never", |
| cache_examples=False, |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
