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import gradio as gr |
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import random |
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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 tensorflow as tf |
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from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation |
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feature_extractor = SegformerFeatureExtractor.from_pretrained( |
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"nvidia/segformer-b5-finetuned-cityscapes-1024-1024" |
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) |
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model = TFSegformerForSemanticSegmentation.from_pretrained( |
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"nvidia/segformer-b5-finetuned-cityscapes-1024-1024" |
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) |
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def palette(): |
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return [ |
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[204, 87, 92], |
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[112, 185, 212], |
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[196, 160, 122], |
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[106, 135, 242], |
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[91, 192, 222], |
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[255, 192, 203], |
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[176, 224, 230], |
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[222, 49, 99], |
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[139, 69, 19], |
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[255, 0, 0], |
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[0, 0, 255], |
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[255, 228, 181], |
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[128, 0, 0], |
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[0, 128, 0], |
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[255, 99, 71], |
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[0, 255, 0], |
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[128, 0, 128], |
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[255, 255, 0], |
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[128, 0, 128] |
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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(palette()) |
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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): |
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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.numpy().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_left() |
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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=27) |
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return fig |
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def sepia(input_img): |
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input_img = Image.fromarray(input_img) |
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inputs = feature_extractor(images=input_img, return_tensors="tf") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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logits = tf.transpose(logits, [0, 2, 3, 1]) |
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logits = tf.image.resize( |
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logits, input_img.size[::-1] |
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) |
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seg = tf.math.argmax(logits, axis=-1)[0] |
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color_seg = np.zeros( |
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(seg.shape[0], seg.shape[1], 3), dtype=np.uint8 |
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) |
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for label, color in enumerate(colormap): |
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color_seg[seg.numpy() == label, :] = color |
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pred_img = np.array(input_img) * 0.5 + color_seg * 0.5 |
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pred_img = pred_img.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(fn=sepia, |
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inputs=gr.Image(shape=(564,846)), |
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outputs=['plot'], |
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live=True, |
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examples=["city1.jpg","city2.jpg","city3.jpg","city4.jpg","city5.jpg"], |
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allow_flagging='never', |
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title="City Image Segmentation Model", |
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theme="huggingfacedark", |
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description="This model is a high-performance city image segmentation model based on the Segformer architecture provided by NVIDIA. Specifically, the 'segformer-b5' model, trained on the Cityscapes dataset, excels at performing intricate segmentation on high-resolution images of 1024x1024 pixels. It accurately identifies various urban elements such as roads, buildings, pedestrians, providing visually rich segmentation results.This is a machine learning activity project at Kyunggi University.", |
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) |
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demo.launch() |
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