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import gradio as gr |
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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-b0-finetuned-ade-512-512" |
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) |
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model = TFSegformerForSemanticSegmentation.from_pretrained( |
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"nvidia/segformer-b0-finetuned-ade-512-512" |
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) |
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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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[111, 214, 93], |
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[18, 181, 57], |
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[72, 152, 135], |
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[240, 74, 253], |
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[211, 22, 184], |
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[68, 111, 215], |
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[120, 212, 135], |
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[185, 244, 20], |
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[190, 90, 92], |
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[53, 18, 220], |
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[251, 56, 67], |
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[141, 248, 248], |
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[226, 38, 196], |
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[153, 75, 248], |
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[158, 166, 127], |
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[240, 254, 73], |
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[157, 99, 218], |
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[85, 243, 54], |
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[38, 71, 123], |
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[207, 188, 66], |
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[145, 24, 6], |
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[187, 252, 239], |
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[240, 181, 229], |
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[137, 187, 112], |
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[104, 219, 158], |
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[234, 56, 176], |
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[23, 141, 13], |
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[28, 22, 88], |
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[83, 169, 127], |
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[1, 236, 221], |
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[61, 88, 81], |
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[102, 94, 10], |
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[116, 233, 66], |
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[147, 247, 143], |
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[241, 72, 39], |
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[229, 165, 195], |
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[22, 247, 217], |
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[110, 208, 164], |
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[236, 236, 6], |
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[163, 31, 15], |
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[78, 148, 190], |
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[92, 222, 66], |
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[198, 120, 99], |
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[161, 201, 28], |
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[235, 88, 53], |
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[249, 233, 102], |
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[235, 115, 89], |
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[51, 135, 171], |
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[37, 162, 46], |
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[11, 200, 171], |
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[192, 186, 65], |
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[173, 208, 139], |
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[240, 124, 1], |
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[106, 209, 96], |
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[174, 126, 239], |
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[221, 234, 164], |
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[140, 46, 109], |
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[135, 62, 174], |
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[130, 51, 242], |
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[229, 28, 133], |
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[30, 157, 217], |
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[154, 195, 123], |
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[157, 115, 35], |
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[199, 218, 59], |
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[144, 47, 157], |
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[253, 185, 226], |
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[8, 62, 238], |
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[71, 191, 146], |
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[217, 227, 170], |
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[169, 195, 73], |
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[253, 60, 179], |
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[42, 239, 174], |
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[67, 221, 248], |
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[163, 179, 218], |
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[250, 30, 153], |
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[154, 66, 181], |
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[109, 228, 192], |
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[213, 212, 73], |
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[125, 186, 185], |
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[12, 80, 88], |
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[188, 90, 227], |
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[38, 131, 95], |
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[105, 56, 175], |
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[230, 72, 244], |
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[212, 98, 68], |
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[5, 14, 131], |
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[136, 150, 164], |
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[72, 70, 198], |
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[160, 124, 189], |
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[255, 132, 160], |
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[199, 71, 86], |
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[32, 209, 66], |
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[167, 50, 228], |
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[163, 72, 61], |
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[53, 24, 145], |
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[132, 27, 124], |
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[72, 143, 166], |
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[54, 156, 177], |
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[197, 26, 37], |
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[230, 92, 201], |
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[31, 47, 165], |
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[133, 215, 89], |
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[190, 51, 145], |
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[162, 3, 41], |
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[37, 197, 236], |
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[247, 19, 29], |
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[105, 12, 99], |
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[130, 235, 57], |
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[112, 224, 59], |
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[6, 253, 14], |
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[205, 176, 152], |
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[110, 202, 51], |
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[94, 74, 61], |
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[108, 86, 56], |
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[148, 184, 162], |
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[125, 0, 195], |
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[143, 211, 60], |
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[108, 240, 95], |
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[106, 211, 59], |
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[12, 1, 158], |
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[46, 53, 36], |
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[130, 192, 113], |
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[204, 224, 85], |
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[162, 86, 98], |
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[10, 155, 230], |
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[76, 105, 166], |
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[157, 34, 206], |
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[3, 230, 115], |
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[115, 172, 117], |
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[98, 2, 191], |
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[173, 132, 102], |
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[3, 47, 51], |
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[60, 7, 102], |
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[70, 47, 237], |
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[10, 145, 167], |
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[235, 156, 244], |
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[142, 188, 86], |
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[137, 45, 182], |
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[110, 37, 249], |
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[21, 108, 156], |
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[51, 19, 187], |
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[66, 99, 230], |
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[249, 153, 221], |
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[231, 146, 194], |
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[153, 115, 50], |
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[25, 15, 226], |
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[126, 9, 119], |
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[241, 114, 28], |
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[134, 156, 64], |
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[111, 215, 120], |
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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(ade_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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unique_labels = np.asarray([]) |
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def draw_plot(pred_img, seg): |
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global unique_labels |
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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_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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global unique_labels |
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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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outputStr = f"이번에는 " |
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for i in unique_labels: |
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outputStr += labels_list[i] + ", " |
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outputStr += "가 검출됐어요." |
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return fig, outputStr |
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demo = gr.Interface( |
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fn=sepia, |
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inputs=gr.Image(shape=(800, 600)), |
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outputs=["plot", "text"], |
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examples=[ |
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"image (1).jpg", |
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"image (2).jpg", |
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"image (3).jpg", |
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"image (4).jpg", |
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"image (5).jpg"], |
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allow_flagging="never", |
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) |
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demo.launch() |
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