diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,1356 +1,247 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - segformer-tf-transformers/apps/gradio/app.py at main 路 deep-diver/segformer-tf-transformers 路 GitHub - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- - - +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-b5-finetuned-ade-640-640" +) +model = TFSegformerForSemanticSegmentation.from_pretrained( + "nvidia/segformer-b5-finetuned-ade-640-640" +) + +def ade_palette(): + """ADE20K palette that maps each class to RGB values.""" + return [ + [120, 120, 120], + [180, 120, 120], + [6, 230, 230], + [80, 50, 50], + [4, 200, 3], + [120, 120, 80], + [140, 140, 140], + [204, 5, 255], + [230, 230, 230], + [4, 250, 7], + [224, 5, 255], + [235, 255, 7], + [150, 5, 61], + [120, 120, 70], + [8, 255, 51], + [255, 6, 82], + [143, 255, 140], + [204, 255, 4], + [255, 51, 7], + [204, 70, 3], + [0, 102, 200], + [61, 230, 250], + [255, 6, 51], + [11, 102, 255], + [255, 7, 71], + [255, 9, 224], + [9, 7, 230], + [220, 220, 220], + [255, 9, 92], + [112, 9, 255], + [8, 255, 214], + [7, 255, 224], + [255, 184, 6], + [10, 255, 71], + [255, 41, 10], + [7, 255, 255], + [224, 255, 8], + [102, 8, 255], + [255, 61, 6], + [255, 194, 7], + [255, 122, 8], + [0, 255, 20], + [255, 8, 41], + [255, 5, 153], + [6, 51, 255], + [235, 12, 255], + [160, 150, 20], + [0, 163, 255], + [140, 140, 140], + [250, 10, 15], + [20, 255, 0], + [31, 255, 0], + [255, 31, 0], + [255, 224, 0], + [153, 255, 0], + [0, 0, 255], + [255, 71, 0], + [0, 235, 255], + [0, 173, 255], + [31, 0, 255], + [11, 200, 200], + [255, 82, 0], + [0, 255, 245], + [0, 61, 255], + [0, 255, 112], + [0, 255, 133], + [255, 0, 0], + [255, 163, 0], + [255, 102, 0], + [194, 255, 0], + [0, 143, 255], + [51, 255, 0], + [0, 82, 255], + [0, 255, 41], + [0, 255, 173], + [10, 0, 255], + [173, 255, 0], + [0, 255, 153], + [255, 92, 0], + [255, 0, 255], + [255, 0, 245], + [255, 0, 102], + [255, 173, 0], + [255, 0, 20], + [255, 184, 184], + [0, 31, 255], + [0, 255, 61], + [0, 71, 255], + [255, 0, 204], + [0, 255, 194], + [0, 255, 82], + [0, 10, 255], + [0, 112, 255], + [51, 0, 255], + [0, 194, 255], + [0, 122, 255], + [0, 255, 163], + [255, 153, 0], + [0, 255, 10], + [255, 112, 0], + [143, 255, 0], + [82, 0, 255], + [163, 255, 0], + [255, 235, 0], + [8, 184, 170], + [133, 0, 255], + [0, 255, 92], + [184, 0, 255], + [255, 0, 31], + [0, 184, 255], + [0, 214, 255], + [255, 0, 112], + [92, 255, 0], + [0, 224, 255], + [112, 224, 255], + [70, 184, 160], + [163, 0, 255], + [153, 0, 255], + [71, 255, 0], + [255, 0, 163], + [255, 204, 0], + [255, 0, 143], + [0, 255, 235], + [133, 255, 0], + [255, 0, 235], + [245, 0, 255], + [255, 0, 122], + [255, 245, 0], + [10, 190, 212], + [214, 255, 0], + [0, 204, 255], + [20, 0, 255], + [255, 255, 0], + [0, 153, 255], + [0, 41, 255], + [0, 255, 204], + [41, 0, 255], + [41, 255, 0], + [173, 0, 255], + [0, 245, 255], + [71, 0, 255], + [122, 0, 255], + [0, 255, 184], + [0, 92, 255], + [184, 255, 0], + [0, 133, 255], + [255, 214, 0], + [25, 194, 194], + [102, 255, 0], + [92, 0, 255], + ] + +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 == label, :] = color + + # Convert to BGR + color_seg = color_seg[..., ::-1] + + # 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 + +demo = gr.Interface(sepia, + gr.Image(shape=(200, 200)), + outputs=['plot'], +# examples=["ADE_val_00000001.jpeg"], + allow_flagging='never') + +demo.launch() \ No newline at end of file