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import gradio as gr |
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from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation |
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import matplotlib.pyplot as plt |
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from matplotlib import gridspec |
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import numpy as np |
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from PIL import Image |
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import tensorflow as tf |
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import requests |
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feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280") |
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model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280") |
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def my_palette(): |
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return [ |
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[131, 162, 255], |
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[180, 189, 255], |
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[255, 227, 187], |
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[255, 210, 143], |
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[248, 117, 170], |
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[255, 223, 223], |
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[255, 246, 246], |
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[174, 222, 252], |
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[150, 194, 145], |
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[255, 219, 170], |
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[244, 238, 238], |
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[50, 38, 83], |
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[128, 98, 214], |
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[146, 136, 248], |
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[255, 210, 215], |
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[255, 152, 152], |
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[162, 103, 138], |
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[63, 29, 56], |
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[0,0,0] |
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] |
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labels_list = [] |
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with open(r"labels.txt", "r") as fp: |
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for line in fp: |
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labels_list.append(line[:-1]) |
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colormap = np.asarray(my_palette()) |
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def greet(input_img): |
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inputs = feature_extractor(images=input_img, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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logits_tf = tf.transpose(logits.detach(), [0, 2, 3, 1]) |
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logits_tf = tf.image.resize( |
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logits_tf, [640, 1280] |
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) |
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seg = tf.math.argmax(logits_tf, axis=-1)[0] |
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color_seg = label_to_color_image(seg.numpy()) |
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color_seg_resized = tf.image.resize(color_seg, (input_img.shape[0], input_img.shape[1])) |
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pred_img = np.array(input_img) * 0.5 + color_seg_resized * 0.5 |
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pred_img = np.array(pred_img).astype(np.uint8) |
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fig = draw_plot(pred_img, seg.numpy()) |
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return fig |
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def draw_plot(pred_img, seg): |
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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.astype("uint8")) |
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unique_labels = unique_labels[unique_labels < len(FULL_COLOR_MAP)] |
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ax = plt.subplot(grid_spec[1]) |
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if len(unique_labels) > 0: |
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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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else: |
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plt.imshow(np.zeros((1, 1, 3), dtype=np.uint8)) |
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ax.yaxis.tick_right() |
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plt.yticks([], []) |
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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 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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label = np.clip(label, 0, len(colormap) - 1) |
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return colormap[label] |
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iface = gr.Interface( |
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fn=greet, |
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inputs="image", |
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outputs=["plot"], |
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examples=["image (1).jpg", "image (2).jpg", "image (3).jpg", "image (4).jpg", "image (5).jpg"], |
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allow_flagging="never" |
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) |
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iface.launch(share=True) |
