Spaces:
Runtime error
Runtime error
| 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 | |
| import requests | |
| 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 [ | |
| [215, 252, 54], | |
| [219, 99, 20], | |
| [30, 125, 246], | |
| [21, 211, 22], | |
| [117, 165, 201], | |
| [122, 2, 6], | |
| [52, 144, 140], | |
| [136, 36, 114], | |
| [208, 249, 44], | |
| [210, 245, 157], | |
| [48, 222, 84], | |
| [175, 182, 112], | |
| [117, 9, 240], | |
| [153, 38, 30], | |
| [75, 225, 231], | |
| [232, 170, 70], | |
| [154, 35, 115], | |
| [45, 61, 35], | |
| [73, 144, 2], | |
| [54, 80, 136], | |
| [143, 200, 212], | |
| [75, 104, 98], | |
| [17, 211, 27], | |
| [205, 195, 241], | |
| [234, 251, 104], | |
| [33, 174, 95], | |
| [160, 174, 99], | |
| [141, 26, 157], | |
| [84, 247, 88], | |
| [19, 248, 198], | |
| [4, 217, 155], | |
| [204, 163, 16], | |
| [148, 209, 143], | |
| [211, 97, 65], | |
| [19, 4, 131], | |
| [40, 196, 45], | |
| [39, 64, 20], | |
| [166, 107, 50], | |
| [108, 103, 78], | |
| [188, 11, 213], | |
| [24, 156, 152], | |
| [230, 162, 223], | |
| [30, 126, 220], | |
| [74, 10, 238], | |
| [186, 128, 227], | |
| [83, 188, 220], | |
| [9, 132, 231], | |
| [96, 99, 79], | |
| [196, 139, 187], | |
| [117, 122, 171], | |
| [0, 156, 220], | |
| [243, 249, 189], | |
| [243, 245, 211], | |
| [103, 146, 83], | |
| [237, 144, 197], | |
| [35, 151, 20], | |
| [15, 61, 139], | |
| [78, 223, 132], | |
| [120, 49, 9], | |
| [67, 160, 234], | |
| [183, 244, 210], | |
| [245, 161, 139], | |
| [57, 70, 189], | |
| [105, 150, 31], | |
| [219, 85, 49], | |
| [206, 81, 97], | |
| [30, 171, 92], | |
| [251, 42, 67], | |
| [121, 183, 220], | |
| [221, 33, 43], | |
| [8, 96, 100], | |
| [76, 149, 53], | |
| [29, 201, 129], | |
| [7, 213, 227], | |
| [143, 93, 153], | |
| [205, 35, 110], | |
| [37, 94, 142], | |
| [131, 157, 110], | |
| [215, 166, 147], | |
| [164, 94, 252], | |
| [179, 108, 233], | |
| [35, 157, 209], | |
| [145, 252, 241], | |
| [155, 60, 40], | |
| [70, 25, 44], | |
| [53, 83, 133], | |
| [150, 42, 191], | |
| [142, 245, 58], | |
| [150, 198, 69], | |
| [0, 139, 86], | |
| [123, 212, 143], | |
| [210, 166, 191], | |
| [148, 194, 130], | |
| [35, 213, 154], | |
| [203, 139, 93], | |
| [59, 86, 45], | |
| [9, 50, 169], | |
| [207, 118, 246], | |
| [200, 82, 65], | |
| [37, 75, 120], | |
| [237, 99, 63], | |
| [168, 145, 190], | |
| [225, 48, 16], | |
| [17, 184, 115], | |
| [224, 124, 15], | |
| [148, 167, 47], | |
| [162, 25, 116], | |
| [154, 90, 36], | |
| [185, 247, 43], | |
| [183, 138, 202], | |
| [64, 96, 117], | |
| [187, 140, 140], | |
| [121, 116, 188], | |
| [252, 251, 162], | |
| [85, 50, 40], | |
| [209, 241, 228], | |
| [30, 41, 95], | |
| [246, 217, 64], | |
| [151, 149, 197], | |
| [117, 42, 205], | |
| [26, 248, 30], | |
| [28, 224, 232], | |
| [228, 89, 96], | |
| [198, 44, 113], | |
| [220, 68, 218], | |
| [59, 85, 210], | |
| [24, 230, 191], | |
| [145, 192, 181], | |
| [132, 189, 92], | |
| [47, 29, 128], | |
| [11, 245, 204], | |
| [182, 79, 207], | |
| [42, 64, 187], | |
| [72, 4, 37], | |
| [105, 67, 133], | |
| [86, 27, 200], | |
| [243, 211, 40], | |
| [150, 136, 40], | |
| [3, 192, 172], | |
| [34, 96, 149], | |
| [32, 108, 56], | |
| [128, 10, 137], | |
| [94, 211, 108], | |
| [78, 250, 243], | |
| [6, 74, 205], | |
| [6, 7, 38], | |
| [161, 26, 40], | |
| [145, 254, 27], | |
| [119, 145, 127], | |
| [13, 82, 153], | |
| ] | |
| 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.numpy() == label, :] = color | |
| # 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(fn=sepia, | |
| inputs=gr.Image(), | |
| outputs=['plot'], | |
| examples=["image-1.jpg", "image-2.jpg", "image-3.jpg", "image-4.jpeg", "image-5.jpg"], | |
| allow_flagging='never') | |
| demo.launch() | |