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import gradio as gr |
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import sys |
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import csv |
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import numpy as np |
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import cv2 |
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from matplotlib import gridspec |
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import matplotlib.pyplot as plt |
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import onnxruntime as ort |
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import wget |
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csv.field_size_limit(sys.maxsize) |
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def ade_palette(): |
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"""ADE20K palette that maps each class to RGB values.""" |
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return [ |
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[120, 120, 120], |
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[180, 120, 120], |
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[6, 230, 230], |
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[80, 50, 50], |
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[4, 200, 3], |
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[120, 120, 80], |
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[140, 140, 140], |
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[204, 5, 255], |
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[230, 230, 230], |
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[4, 250, 7], |
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[224, 5, 255], |
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[235, 255, 7], |
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[150, 5, 61], |
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[120, 120, 70], |
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[8, 255, 51], |
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[255, 6, 82], |
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[143, 255, 140], |
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[204, 255, 4], |
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[255, 51, 7], |
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[204, 70, 3], |
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[0, 102, 200], |
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[61, 230, 250], |
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[255, 6, 51], |
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[11, 102, 255], |
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[255, 7, 71], |
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[255, 9, 224], |
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[9, 7, 230], |
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[220, 220, 220], |
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[255, 9, 92], |
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[112, 9, 255], |
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[8, 255, 214], |
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[7, 255, 224], |
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[255, 184, 6], |
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[10, 255, 71], |
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[255, 41, 10], |
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[7, 255, 255], |
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[224, 255, 8], |
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[102, 8, 255], |
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[255, 61, 6], |
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[255, 194, 7], |
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[255, 122, 8], |
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[0, 255, 20], |
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[255, 8, 41], |
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[255, 5, 153], |
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[6, 51, 255], |
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[235, 12, 255], |
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[160, 150, 20], |
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[0, 163, 255], |
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[140, 140, 140], |
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[250, 10, 15], |
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[20, 255, 0], |
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[31, 255, 0], |
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[255, 31, 0], |
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[255, 224, 0], |
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[153, 255, 0], |
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[0, 0, 255], |
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[255, 71, 0], |
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[0, 235, 255], |
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[0, 173, 255], |
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[31, 0, 255], |
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[11, 200, 200], |
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[255, 82, 0], |
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[0, 255, 245], |
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[0, 61, 255], |
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[0, 255, 112], |
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[0, 255, 133], |
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[255, 0, 0], |
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[255, 163, 0], |
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[255, 102, 0], |
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[194, 255, 0], |
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[0, 143, 255], |
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[51, 255, 0], |
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[0, 82, 255], |
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[0, 255, 41], |
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[0, 255, 173], |
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[10, 0, 255], |
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[173, 255, 0], |
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[0, 255, 153], |
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[255, 92, 0], |
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[255, 0, 255], |
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[255, 0, 245], |
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[255, 0, 102], |
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[255, 173, 0], |
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[255, 0, 20], |
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[255, 184, 184], |
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[0, 31, 255], |
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[0, 255, 61], |
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[0, 71, 255], |
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[255, 0, 204], |
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[0, 255, 194], |
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[0, 255, 82], |
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[0, 10, 255], |
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[0, 112, 255], |
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[51, 0, 255], |
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[0, 194, 255], |
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[0, 122, 255], |
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[0, 255, 163], |
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[255, 153, 0], |
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[0, 255, 10], |
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[255, 112, 0], |
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[143, 255, 0], |
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[82, 0, 255], |
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[163, 255, 0], |
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[255, 235, 0], |
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[8, 184, 170], |
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[133, 0, 255], |
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[0, 255, 92], |
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[184, 0, 255], |
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[255, 0, 31], |
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[0, 184, 255], |
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[0, 214, 255], |
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[255, 0, 112], |
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[92, 255, 0], |
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[0, 224, 255], |
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[112, 224, 255], |
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[70, 184, 160], |
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[163, 0, 255], |
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[153, 0, 255], |
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[71, 255, 0], |
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[255, 0, 163], |
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[255, 204, 0], |
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[255, 0, 143], |
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[0, 255, 235], |
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[133, 255, 0], |
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[255, 0, 235], |
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[245, 0, 255], |
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[255, 0, 122], |
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[255, 245, 0], |
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[10, 190, 212], |
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[214, 255, 0], |
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[0, 204, 255], |
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[20, 0, 255], |
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[255, 255, 0], |
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[0, 153, 255], |
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[0, 41, 255], |
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[0, 255, 204], |
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[41, 0, 255], |
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[41, 255, 0], |
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[173, 0, 255], |
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[0, 245, 255], |
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[71, 0, 255], |
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[122, 0, 255], |
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[0, 255, 184], |
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[0, 92, 255], |
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[184, 255, 0], |
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[0, 133, 255], |
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[255, 214, 0], |
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[25, 194, 194], |
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[102, 255, 0], |
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[92, 0, 255], |
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] |
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url='https://github.com/deep-diver/segformer-tf-transformers/releases/download/1.0/segformer-b5-finetuned-ade-640-640.onnx' |
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labels_list = [] |
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colormap = np.asarray(ade_palette()) |
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model_path = wget.download(url) |
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sess = ort.InferenceSession(model_path) |
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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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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) |
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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_right() |
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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=25) |
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return fig |
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def sepia(input_img): |
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img = cv2.imread(input_img).astype(np.float32) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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img_batch = np.expand_dims(img, axis=0) |
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img_batch = np.transpose(img_batch, (0, 3, 1, 2)) |
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logits = sess.run(None, {"pixel_values": img_batch})[0] |
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logits = np.transpose(logits, (0, 2, 3, 1)) |
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seg = np.argmax(logits, axis=-1)[0].astype('float32') |
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seg = cv2.resize(seg, (640, 640)).astype('uint8') |
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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 == label, :] = color |
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color_seg = color_seg[..., ::-1] |
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pred_img = 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(sepia, |
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gr.inputs.Image(type="filepath", shape=(640, 640)), |
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outputs=['plot'], |
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examples=["ADE_val_00000001.jpeg"], |
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allow_flagging='never') |
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demo.launch() |
