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| #-*- encoding: utf-8 -*- | |
| import gradio as gr | |
| from matplotlib import gridspec | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from PIL import Image | |
| import tensorflow as tf | |
| from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation | |
| feature_extractor = SegformerFeatureExtractor.from_pretrained( | |
| "nvidia/segformer-b4-finetuned-cityscapes-1024-1024" | |
| ) | |
| model = TFSegformerForSemanticSegmentation.from_pretrained( | |
| "nvidia/segformer-b4-finetuned-cityscapes-1024-1024" | |
| ) | |
| def ade_palette(): | |
| """ADE20K palette that maps each class to RGB values.""" | |
| return [ | |
| [0, 0, 0], # black | |
| [140, 140, 140], # gray | |
| [95, 0, 255], # purple | |
| [221, 126, 255], # light purple | |
| [1, 0, 255], # blue | |
| [0, 216, 255], # light blue | |
| [35, 164, 26], # green | |
| [29, 219, 22], # light green | |
| [255, 228, 0], # yellow | |
| [255, 187, 0], # light orange | |
| [255, 94, 0], # orange | |
| [255, 0, 0], # red | |
| [255, 167, 167], # pink | |
| [153, 56, 0], # brown | |
| [207, 166, 54], | |
| [180, 40, 180], | |
| [120, 56, 123], | |
| [45, 56, 28], | |
| [67, 56, 123], | |
| ] | |
| labels_list = [] | |
| with open(r'labels.txt', 'r') as fp: | |
| for line in fp: | |
| labels_list.append(line[:-1]) | |
| colormap = np.asarray(ade_palette()) | |
| 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): | |
| 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.numpy().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 sepia(input_img): | |
| input_img = Image.fromarray(input_img) | |
| inputs = feature_extractor(images=input_img, return_tensors="tf") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| logits = tf.transpose(logits, [0, 2, 3, 1]) | |
| logits = tf.image.resize( | |
| logits, input_img.size[::-1] | |
| ) # We reverse the shape of `image` because `image.size` returns width and height. | |
| seg = tf.math.argmax(logits, axis=-1)[0] | |
| color_seg = np.zeros( | |
| (seg.shape[0], seg.shape[1], 3), dtype=np.uint8 | |
| ) # height, width, 3 | |
| for label, color in enumerate(colormap): | |
| color_seg[seg.numpy() == label, :] = color | |
| # Show image + mask | |
| pred_img = np.array(input_img) * 0.5 + color_seg * 0.5 | |
| pred_img = pred_img.astype(np.uint8) | |
| fig = draw_plot(pred_img, seg) | |
| return fig | |
| with gr.Blocks(theme=gr.themes.Monochrome()) as demo: | |
| with gr.Tab("Semantic Segmentation with Cityscape Image"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| input_gallery = gr.Gallery(label="Select an Image", | |
| value="city_1.jpg", | |
| items=["city_1.jpg", "city_2.jpg", "city_3.jpg", | |
| "city_4.jpg", "city_5.jpg", "city_6.jpg", | |
| "city_7.jpg", "city_8.jpg"]).style(grid=3) | |
| input_image = gr.Image(label="Uploaded Image", interactive=True, type="pil") | |
| input_gallery.change(fn=lambda x: x, inputs=input_gallery, outputs=input_image) | |
| process_button = gr.Button("Process Image") | |
| with gr.Column(scale=2): | |
| output_image = gr.Plot(label="Segmented Image") | |
| process_button.click(sepia, inputs=input_image, outputs=output_image) | |
| with gr.Accordion("Information"): | |
| gr.Markdown("A Gradio-based page which performs Semantic Segmentation into 19 classes for an example image") | |
| demo.launch() | |