Spaces:
Sleeping
Sleeping
| 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 = "nvidia/segformer-b5-finetuned-ade-640-640" | |
| 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], | |
| [156, 200, 56],[32, 90, 210],[56, 123, 67],[180, 56, 123],[123, 67, 45],[45, 134, 200], | |
| [67, 56, 123],[78, 123, 67],[32, 210, 90],[45, 56, 189],[123, 56, 123],[56, 156, 200], | |
| [189, 56, 45],[112, 200, 56],[56, 123, 45],[200, 32, 90],[123, 45, 78],[200, 156, 56], | |
| [45, 67, 123],[56, 45, 78],[45, 56, 123],[123, 67, 56],[56, 78, 123],[210, 90, 32], | |
| [123, 56, 189],[45, 200, 134],[67, 123, 56],[123, 45, 67],[90, 32, 210],[200, 45, 78], | |
| [32, 210, 90],[45, 123, 67],[165, 42, 87],[72, 145, 167],[15, 158, 75],[209, 89, 40], | |
| [32, 21, 121],[184, 20, 100],[56, 135, 15],[128, 92, 176],[1, 119, 140],[220, 151, 43], | |
| [41, 97, 72],[148, 38, 27],[107, 86, 176],[21, 26, 136],[174, 27, 90],[91, 96, 204], | |
| [108, 50, 107],[27, 45, 136],[168, 200, 52],[7, 102, 27],[42, 93, 56],[140, 52, 112], | |
| [92, 107, 168],[17, 118, 176],[59, 50, 174],[206, 40, 143],[44, 19, 142],[23, 168, 75], | |
| [54, 57, 189],[144, 21, 15],[15, 176, 35],[107, 19, 79],[204, 52, 114],[48, 173, 83], | |
| [11, 120, 53],[206, 104, 28],[20, 31, 153],[27, 21, 93],[11, 206, 138],[112, 30, 83], | |
| [68, 91, 152],[153, 13, 43],[25, 114, 54],[92, 27, 150],[108, 42, 59],[194, 77, 5], | |
| [145, 48, 83],[7, 113, 19],[25, 92, 113],[60, 168, 79],[78, 33, 120],[89, 176, 205], | |
| [27, 200, 94],[210, 67, 23],[123, 89, 189],[225, 56, 112],[75, 156, 45],[172, 104, 200], | |
| [15, 170, 197],[240, 133, 65],[89, 156, 112],[214, 88, 57],[156, 134, 200],[78, 57, 189], | |
| [200, 78, 123],[106, 120, 210],[145, 56, 112],[89, 120, 189],[185, 206, 56],[47, 99, 28], | |
| [112, 189, 78],[200, 112, 89],[89, 145, 112],[78, 106, 189],[112, 78, 189],[156, 112, 78], | |
| [28, 210, 99],[78, 89, 189],[189, 78, 57],[112, 200, 78],[189, 47, 78],[205, 112, 57], | |
| [78, 145, 57],[200, 78, 112],[99, 89, 145],[200, 156, 78],[57, 78, 145],[78, 57, 99], | |
| [57, 78, 145],[145, 112, 78],[78, 89, 145],[210, 99, 28],[145, 78, 189],[57, 200, 136], | |
| [89, 156, 78],[145, 78, 99],[99, 28, 210],[189, 78, 47],[28, 210, 99],[78, 145, 57], | |
| ] | |
| 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): | |
| # input: numpy array from gradio -> PIL | |
| 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 # (1, C, h/4, w/4) | |
| # resize to original | |
| 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) # (H,W) | |
| # colorize & overlay | |
| color_seg = colormap[seg] # (H,W,3) | |
| 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=[ | |
| "ADE_val_00000001.jpeg", | |
| "ADE_val_00001159.jpg", | |
| "ADE_val_00001248.jpg", | |
| "ADE_val_00001472.jpg" | |
| ], | |
| flagging_mode="never", | |
| cache_examples=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |