lds204/week9

1import gradio as gr2 3from matplotlib import gridspec4import matplotlib.pyplot as plt5import numpy as np6from PIL import Image7import tensorflow as tf8from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation9 10feature_extractor = SegformerFeatureExtractor.from_pretrained(11    "mattmdjaga/segformer_b2_clothes"12)13model = TFSegformerForSemanticSegmentation.from_pretrained(14    "mattmdjaga/segformer_b2_clothes"15)16 17def ade_palette():18    """ADE20K palette that maps each class to RGB values."""19    return [20        [204, 87, 92],21        [112, 185, 212],22        [45, 189, 106],23        [234, 123, 67],24        [78, 56, 123],25        [210, 32, 89],26        [90, 180, 56],27        [155, 102, 200],28        [33, 147, 176],29        [255, 183, 76],30        [67, 123, 89],31        [190, 60, 45],32        [134, 112, 200],33        [56, 45, 189],34        [200, 56, 123],35        [87, 92, 204],36        [120, 56, 123],37        [45, 78, 123],38        [156, 200, 56],39        [32, 90, 210],40        [56, 123, 67],41        [180, 56, 123],42        [123, 67, 45],43        [45, 134, 200],44        [67, 56, 123],45        [78, 123, 67],46        [32, 210, 90],47        [45, 56, 189],48        [123, 56, 123],49        [56, 156, 200],50        [189, 56, 45],51        [112, 200, 56],52        [56, 123, 45],53        [200, 32, 90],54        [123, 45, 78],55        [200, 156, 56],56        [45, 67, 123],57        [56, 45, 78],58        [45, 56, 123],59        [123, 67, 56],60        [56, 78, 123],61        [210, 90, 32],62        [123, 56, 189],63        [45, 200, 134],64        [67, 123, 56],65        [123, 45, 67],66        [90, 32, 210],67        [200, 45, 78],68        [32, 210, 90],69        [45, 123, 67],70        [165, 42, 87],71        [72, 145, 167],72        [15, 158, 75],73        [209, 89, 40],74        [32, 21, 121],75        [184, 20, 100],76        [56, 135, 15],77        [128, 92, 176],78        [1, 119, 140],79        [220, 151, 43],80        [41, 97, 72],81        [148, 38, 27],82        [107, 86, 176],83        [21, 26, 136],84        [174, 27, 90],85        [91, 96, 204],86        [108, 50, 107],87        [27, 45, 136],88        [168, 200, 52],89        [7, 102, 27],90        [42, 93, 56],91        [140, 52, 112],92        [92, 107, 168],93        [17, 118, 176],94        [59, 50, 174],95        [206, 40, 143],96        [44, 19, 142],97        [23, 168, 75],98        [54, 57, 189],99        [144, 21, 15],100        [15, 176, 35],101        [107, 19, 79],102        [204, 52, 114],103        [48, 173, 83],104        [11, 120, 53],105        [206, 104, 28],106        [20, 31, 153],107        [27, 21, 93],108        [11, 206, 138],109        [112, 30, 83],110        [68, 91, 152],111        [153, 13, 43],112        [25, 114, 54],113        [92, 27, 150],114        [108, 42, 59],115        [194, 77, 5],116        [145, 48, 83],117        [7, 113, 19],118        [25, 92, 113],119        [60, 168, 79],120        [78, 33, 120],121        [89, 176, 205],122        [27, 200, 94],123        [210, 67, 23],124        [123, 89, 189],125        [225, 56, 112],126        [75, 156, 45],127        [172, 104, 200],128        [15, 170, 197],129        [240, 133, 65],130        [89, 156, 112],131        [214, 88, 57],132        [156, 134, 200],133        [78, 57, 189],134        [200, 78, 123],135        [106, 120, 210],136        [145, 56, 112],137        [89, 120, 189],138        [185, 206, 56],139        [47, 99, 28],140        [112, 189, 78],141        [200, 112, 89],142        [89, 145, 112],143        [78, 106, 189],144        [112, 78, 189],145        [156, 112, 78],146        [28, 210, 99],147        [78, 89, 189],148        [189, 78, 57],149        [112, 200, 78],150        [189, 47, 78],151        [205, 112, 57],152        [78, 145, 57],153        [200, 78, 112],154        [99, 89, 145],155        [200, 156, 78],156        [57, 78, 145],157        [78, 57, 99],158        [57, 78, 145],159        [145, 112, 78],160        [78, 89, 145],161        [210, 99, 28],162        [145, 78, 189],163        [57, 200, 136],164        [89, 156, 78],165        [145, 78, 99],166        [99, 28, 210],167        [189, 78, 47],168        [28, 210, 99],169        [78, 145, 57],170    ]171 172labels_list = []173 174with open(r'labels.txt', 'r') as fp:175    for line in fp:176        labels_list.append(line[:-1])177 178colormap = np.asarray(ade_palette())179 180def label_to_color_image(label):181    if label.ndim != 2:182        raise ValueError("Expect 2-D input label")183 184    if np.max(label) >= len(colormap):185        raise ValueError("label value too large.")186    return colormap[label]187 188def draw_plot(pred_img, seg):189    fig = plt.figure(figsize=(20, 15))190 191    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])192 193    plt.subplot(grid_spec[0])194    plt.imshow(pred_img)195    plt.axis('off')196    LABEL_NAMES = np.asarray(labels_list)197    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)198    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)199 200    unique_labels = np.unique(seg.numpy().astype("uint8"))201    ax = plt.subplot(grid_spec[1])202    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")203    ax.yaxis.tick_right()204    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])205    plt.xticks([], [])206    ax.tick_params(width=0.0, labelsize=25)207    return fig208 209def sepia(input_img):210    input_img = Image.fromarray(input_img)211 212    inputs = feature_extractor(images=input_img, return_tensors="tf")213    outputs = model(**inputs)214    logits = outputs.logits215 216    logits = tf.transpose(logits, [0, 2, 3, 1])217    logits = tf.image.resize(218        logits, input_img.size[::-1]219    )  # We reverse the shape of `image` because `image.size` returns width and height.220    seg = tf.math.argmax(logits, axis=-1)[0]221 222    color_seg = np.zeros(223        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8224    )  # height, width, 3225    for label, color in enumerate(colormap):226        color_seg[seg.numpy() == label, :] = color227 228    # Show image + mask229    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5230    pred_img = pred_img.astype(np.uint8)231 232    fig = draw_plot(pred_img, seg)233    return fig234 235demo = gr.Interface(fn=sepia,236                    inputs=gr.Image(shape=(400, 600)),237                    outputs=['plot'],238                    examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg","person-5.jpg"],239                    allow_flagging='never')240 241 242demo.launch()243