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def sepia(input_img, intensity): |
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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-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 ade_palette(): |
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"""ADE20K palette that maps each class to RGB values.""" |
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return [ |
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[0, 0, 0], |
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[255, 0, 0], |
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[255, 255, 0], |
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[255, 255, 255], |
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[255, 0, 255], |
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[0, 255, 0], |
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[0, 255, 255], |
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[0, 0, 255], |
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[33, 147, 176], |
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[255, 183, 76], |
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[67, 123, 89], |
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[190, 60, 45], |
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[134, 112, 200], |
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[56, 45, 189], |
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[200, 56, 123], |
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[87, 92, 204], |
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[120, 56, 123], |
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[45, 78, 123], |
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[156, 200, 56], |
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[32, 90, 210], |
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[56, 123, 67], |
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[180, 56, 123], |
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[123, 67, 45], |
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[45, 134, 200], |
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[67, 56, 123], |
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[78, 123, 67], |
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[32, 210, 90], |
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[45, 56, 189], |
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[123, 56, 123], |
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[56, 156, 200], |
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[189, 56, 45], |
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[112, 200, 56], |
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[56, 123, 45], |
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[200, 32, 90], |
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[123, 45, 78], |
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[200, 156, 56], |
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[45, 67, 123], |
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[56, 45, 78], |
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[45, 56, 123], |
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[123, 67, 56], |
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[56, 78, 123], |
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[210, 90, 32], |
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[123, 56, 189], |
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[45, 200, 134], |
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[67, 123, 56], |
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[123, 45, 67], |
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[90, 32, 210], |
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[200, 45, 78], |
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[32, 210, 90], |
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[45, 123, 67], |
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[165, 42, 87], |
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[72, 145, 167], |
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[15, 158, 75], |
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[209, 89, 40], |
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[32, 21, 121], |
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[184, 20, 100], |
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[56, 135, 15], |
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[128, 92, 176], |
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[1, 119, 140], |
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[220, 151, 43], |
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[41, 97, 72], |
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[148, 38, 27], |
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[107, 86, 176], |
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[21, 26, 136], |
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[174, 27, 90], |
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[91, 96, 204], |
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[108, 50, 107], |
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[27, 45, 136], |
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[168, 200, 52], |
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[7, 102, 27], |
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[42, 93, 56], |
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[140, 52, 112], |
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[92, 107, 168], |
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[17, 118, 176], |
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[59, 50, 174], |
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[206, 40, 143], |
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[44, 19, 142], |
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[23, 168, 75], |
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[54, 57, 189], |
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[144, 21, 15], |
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[15, 176, 35], |
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[107, 19, 79], |
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[204, 52, 114], |
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[48, 173, 83], |
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[11, 120, 53], |
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[206, 104, 28], |
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[20, 31, 153], |
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[27, 21, 93], |
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[11, 206, 138], |
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[112, 30, 83], |
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[68, 91, 152], |
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[153, 13, 43], |
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[25, 114, 54], |
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[92, 27, 150], |
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[108, 42, 59], |
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[194, 77, 5], |
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[145, 48, 83], |
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[7, 113, 19], |
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[25, 92, 113], |
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[60, 168, 79], |
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[78, 33, 120], |
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[89, 176, 205], |
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[27, 200, 94], |
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[210, 67, 23], |
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[123, 89, 189], |
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[225, 56, 112], |
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[75, 156, 45], |
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[172, 104, 200], |
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[15, 170, 197], |
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[240, 133, 65], |
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[89, 156, 112], |
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[214, 88, 57], |
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[156, 134, 200], |
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[78, 57, 189], |
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[200, 78, 123], |
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[106, 120, 210], |
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[145, 56, 112], |
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[89, 120, 189], |
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[185, 206, 56], |
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[47, 99, 28], |
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[112, 189, 78], |
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[200, 112, 89], |
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[89, 145, 112], |
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[78, 106, 189], |
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[112, 78, 189], |
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[156, 112, 78], |
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[28, 210, 99], |
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[78, 89, 189], |
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[189, 78, 57], |
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[112, 200, 78], |
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[189, 47, 78], |
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[205, 112, 57], |
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[78, 145, 57], |
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[200, 78, 112], |
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[99, 89, 145], |
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[200, 156, 78], |
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[57, 78, 145], |
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[78, 57, 99], |
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[57, 78, 145], |
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[145, 112, 78], |
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[78, 89, 145], |
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[210, 99, 28], |
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[145, 78, 189], |
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[57, 200, 136], |
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[89, 156, 78], |
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[145, 78, 99], |
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[99, 28, 210], |
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[189, 78, 47], |
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[28, 210, 99], |
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[78, 145, 57], |
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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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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_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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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) * (1 - intensity) + color_seg * intensity |
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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( |
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fn=sepia, |
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inputs=[gr.inputs.Image(shape=(400, 600)), gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5)], |
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outputs='plot', |
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examples=[["cheonggyecheon_stream_in_seoul_city.jpg", 0.5], ["Incheon_stadium.jpg", 0.7], |
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["Incheon_city.jpg", 0.3]], |
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title="Semantic Segmentation", |
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description="This is a demo of semantic segmentation using Segformer.", |
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allow_flagging=False, |
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) |
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demo.launch() |
