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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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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[116, 116, 116], |
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[47, 157, 39], |
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[217, 65, 140], |
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[255, 255, 255], |
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[255, 178, 245], |
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[61, 183, 204], |
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[153, 0, 76], |
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[71, 102, 0], |
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[255, 167, 167], |
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[25, 25, 25], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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[92, 209, 229], |
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[153, 56, 0], |
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[128, 65, 217], |
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[53, 53, 53], |
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[0, 84, 255], |
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[171, 242, 0], |
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[255, 0, 221], |
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[255, 228, 0], |
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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) * 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=(512, 512)), |
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outputs=['plot'], |
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examples=["image1.jpg", "image2.jpg", "image3.jpg", "image4.jpg"], |
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allow_flagging='never' |
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) |
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demo.launch()import gradio as gr |
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def update(name): |
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return f"Welcome to Gradio, {name}!" |
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with gr.Blocks() as visit: |
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gr.Markdown("Start typing below and then click **방명록** to see the output.") |
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with gr.Row(): |
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inp = gr.Textbox(placeholder="방문을 환영합니다. 성함이 어떻게 되세요?") |
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out = gr.Textbox() |
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btn = gr.Button("방명록") |
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btn.click(fn=update, inputs=inp, outputs=out) |
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demo.launch() |
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