| 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-b0-finetuned-ade-512-512" |
| ) |
| model = TFSegformerForSemanticSegmentation.from_pretrained( |
| "nvidia/segformer-b0-finetuned-ade-512-512" |
| ) |
|
|
|
|
| def ade_palette(): |
| """ADE20K palette that maps each class to RGB values.""" |
| return [ |
| [111, 214, 93], |
| [18, 181, 57], |
| [72, 152, 135], |
| [240, 74, 253], |
| [211, 22, 184], |
| [68, 111, 215], |
| [120, 212, 135], |
| [185, 244, 20], |
| [190, 90, 92], |
| [53, 18, 220], |
| [251, 56, 67], |
| [141, 248, 248], |
| [226, 38, 196], |
| [153, 75, 248], |
| [158, 166, 127], |
| [240, 254, 73], |
| [157, 99, 218], |
| [85, 243, 54], |
| [38, 71, 123], |
| [207, 188, 66], |
| [145, 24, 6], |
| [187, 252, 239], |
| [240, 181, 229], |
| [137, 187, 112], |
| [104, 219, 158], |
| [234, 56, 176], |
| [23, 141, 13], |
| [28, 22, 88], |
| [83, 169, 127], |
| [1, 236, 221], |
| [61, 88, 81], |
| [102, 94, 10], |
| [116, 233, 66], |
| [147, 247, 143], |
| [241, 72, 39], |
| [229, 165, 195], |
| [22, 247, 217], |
| [110, 208, 164], |
| [236, 236, 6], |
| [163, 31, 15], |
| [78, 148, 190], |
| [92, 222, 66], |
| [198, 120, 99], |
| [161, 201, 28], |
| [235, 88, 53], |
| [249, 233, 102], |
| [235, 115, 89], |
| [51, 135, 171], |
| [37, 162, 46], |
| [11, 200, 171], |
| [192, 186, 65], |
| [173, 208, 139], |
| [240, 124, 1], |
| [106, 209, 96], |
| [174, 126, 239], |
| [221, 234, 164], |
| [140, 46, 109], |
| [135, 62, 174], |
| [130, 51, 242], |
| [229, 28, 133], |
| [30, 157, 217], |
| [154, 195, 123], |
| [157, 115, 35], |
| [199, 218, 59], |
| [144, 47, 157], |
| [253, 185, 226], |
| [8, 62, 238], |
| [71, 191, 146], |
| [217, 227, 170], |
| [169, 195, 73], |
| [253, 60, 179], |
| [42, 239, 174], |
| [67, 221, 248], |
| [163, 179, 218], |
| [250, 30, 153], |
| [154, 66, 181], |
| [109, 228, 192], |
| [213, 212, 73], |
| [125, 186, 185], |
| [12, 80, 88], |
| [188, 90, 227], |
| [38, 131, 95], |
| [105, 56, 175], |
| [230, 72, 244], |
| [212, 98, 68], |
| [5, 14, 131], |
| [136, 150, 164], |
| [72, 70, 198], |
| [160, 124, 189], |
| [255, 132, 160], |
| [199, 71, 86], |
| [32, 209, 66], |
| [167, 50, 228], |
| [163, 72, 61], |
| [53, 24, 145], |
| [132, 27, 124], |
| [72, 143, 166], |
| [54, 156, 177], |
| [197, 26, 37], |
| [230, 92, 201], |
| [31, 47, 165], |
| [133, 215, 89], |
| [190, 51, 145], |
| [162, 3, 41], |
| [37, 197, 236], |
| [247, 19, 29], |
| [105, 12, 99], |
| [130, 235, 57], |
| [112, 224, 59], |
| [6, 253, 14], |
| [205, 176, 152], |
| [110, 202, 51], |
| [94, 74, 61], |
| [108, 86, 56], |
| [148, 184, 162], |
| [125, 0, 195], |
| [143, 211, 60], |
| [108, 240, 95], |
| [106, 211, 59], |
| [12, 1, 158], |
| [46, 53, 36], |
| [130, 192, 113], |
| [204, 224, 85], |
| [162, 86, 98], |
| [10, 155, 230], |
| [76, 105, 166], |
| [157, 34, 206], |
| [3, 230, 115], |
| [115, 172, 117], |
| [98, 2, 191], |
| [173, 132, 102], |
| [3, 47, 51], |
| [60, 7, 102], |
| [70, 47, 237], |
| [10, 145, 167], |
| [235, 156, 244], |
| [142, 188, 86], |
| [137, 45, 182], |
| [110, 37, 249], |
| [21, 108, 156], |
| [51, 19, 187], |
| [66, 99, 230], |
| [249, 153, 221], |
| [231, 146, 194], |
| [153, 115, 50], |
| [25, 15, 226], |
| [126, 9, 119], |
| [241, 114, 28], |
| [134, 156, 64], |
| [111, 215, 120], |
| ] |
|
|
|
|
| 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] |
| ) |
| seg = tf.math.argmax(logits, axis=-1)[0] |
|
|
| color_seg = np.zeros( |
| (seg.shape[0], seg.shape[1], 3), dtype=np.uint8 |
| ) |
| for label, color in enumerate(colormap): |
| color_seg[seg.numpy() == label, :] = color |
|
|
| |
| 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(shape=(800, 600)), |
| outputs=["plot"], |
| examples=[ |
| "image (1).jpg"], |
| allow_flagging="never", |
| ) |
|
|
| demo.launch() |
|
|
