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| import gradio as gr | |
| import pandas as pd | |
| 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 = [ | |
| 'wall', | |
| 'building;edifice', | |
| 'sky', | |
| 'floor;flooring', | |
| 'tree', | |
| 'ceiling', | |
| 'road;route', | |
| 'bed', | |
| 'windowpane;window', | |
| 'grass', | |
| 'cabinet', | |
| 'sidewalk;pavement', | |
| 'person;individual;someone;somebody;mortal;soul', | |
| 'earth;ground', | |
| 'door;double;door', | |
| 'table', | |
| 'mountain;mount', | |
| 'plant;flora;plant;life', | |
| 'curtain;drape;drapery;mantle;pall', | |
| 'chair', | |
| 'car;auto;automobile;machine;motorcar', | |
| 'water', | |
| 'painting;picture', | |
| 'sofa;couch;lounge', | |
| 'shelf', | |
| 'house', | |
| 'sea', | |
| 'mirror', | |
| 'rug;carpet;carpeting', | |
| 'field', | |
| 'armchair', | |
| 'seat', | |
| 'fence;fencing', | |
| 'desk', | |
| 'rock;stone', | |
| 'wardrobe;closet;press', | |
| 'lamp', | |
| 'bathtub;bathing;tub;bath;tub', | |
| 'railing;rail', | |
| 'cushion', | |
| 'base;pedestal;stand', | |
| 'box', | |
| 'column;pillar', | |
| 'signboard;sign', | |
| 'chest;of;drawers;chest;bureau;dresser', | |
| 'counter', | |
| 'sand', | |
| 'sink', | |
| 'skyscraper', | |
| 'fireplace;hearth;open;fireplace', | |
| 'refrigerator;icebox', | |
| 'grandstand;covered;stand', | |
| 'path', | |
| 'stairs;steps', | |
| 'runway', | |
| 'case;display;case;showcase;vitrine', | |
| 'pool;table;billiard;table;snooker;table', | |
| 'pillow', | |
| 'screen;door;screen', | |
| 'stairway;staircase', | |
| 'river', | |
| 'bridge;span', | |
| 'bookcase', | |
| 'blind;screen', | |
| 'coffee;table;cocktail;table', | |
| 'toilet;can;commode;crapper;pot;potty;stool;throne', | |
| 'flower', | |
| 'book', | |
| 'hill', | |
| 'bench', | |
| 'countertop', | |
| 'stove;kitchen;stove;range;kitchen;range;cooking;stove', | |
| 'palm;palm;tree', | |
| 'kitchen;island', | |
| 'computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system', | |
| 'swivel;chair', | |
| 'boat', | |
| 'bar', | |
| 'arcade;machine', | |
| 'hovel;hut;hutch;shack;shanty', | |
| 'bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle', | |
| 'towel', | |
| 'light;light;source', | |
| 'truck;motortruck', | |
| 'tower', | |
| 'chandelier;pendant;pendent', | |
| 'awning;sunshade;sunblind', | |
| 'streetlight;street;lamp', | |
| 'booth;cubicle;stall;kiosk', | |
| 'television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box', | |
| 'airplane;aeroplane;plane', | |
| 'dirt;track', | |
| 'apparel;wearing;apparel;dress;clothes', | |
| 'pole', | |
| 'land;ground;soil', | |
| 'bannister;banister;balustrade;balusters;handrail', | |
| 'escalator;moving;staircase;moving;stairway', | |
| 'ottoman;pouf;pouffe;puff;hassock', | |
| 'bottle', | |
| 'buffet;counter;sideboard', | |
| 'poster;posting;placard;notice;bill;card', | |
| 'stage', | |
| 'van', | |
| 'ship', | |
| 'fountain', | |
| 'conveyer;belt;conveyor;belt;conveyer;conveyor;transporter', | |
| 'canopy', | |
| 'washer;automatic;washer;washing;machine', | |
| 'plaything;toy', | |
| 'swimming;pool;swimming;bath;natatorium', | |
| 'stool', | |
| 'barrel;cask', | |
| 'basket;handbasket', | |
| 'waterfall;falls', | |
| 'tent;collapsible;shelter', | |
| 'bag', | |
| 'minibike;motorbike', | |
| 'cradle', | |
| 'oven', | |
| 'ball', | |
| 'food;solid;food', | |
| 'step;stair', | |
| 'tank;storage;tank', | |
| 'trade;name;brand;name;brand;marque', | |
| 'microwave;microwave;oven', | |
| 'pot;flowerpot', | |
| 'animal;animate;being;beast;brute;creature;fauna', | |
| 'bicycle;bike;wheel;cycle', | |
| 'lake', | |
| 'dishwasher;dish;washer;dishwashing;machine', | |
| 'screen;silver;screen;projection;screen', | |
| 'blanket;cover', | |
| 'sculpture', | |
| 'hood;exhaust;hood', | |
| 'sconce', | |
| 'vase', | |
| 'traffic;light;traffic;signal;stoplight', | |
| 'tray', | |
| 'ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin', | |
| 'fan', | |
| 'pier;wharf;wharfage;dock', | |
| 'crt;screen', | |
| 'plate', | |
| 'monitor;monitoring;device', | |
| 'bulletin;board;notice;board', | |
| 'shower', | |
| 'radiator', | |
| 'glass;drinking;glass', | |
| 'clock', | |
| 'flag'] | |
| def label_to_color_image(label): | |
| """Adds color defined by the dataset colormap to the label. | |
| Args: | |
| label: A 2D array with integer type, storing the segmentation label. | |
| Returns: | |
| result: A 2D array with floating type. The element of the array | |
| is the color indexed by the corresponding element in the input label | |
| to the PASCAL color map. | |
| Raises: | |
| ValueError: If label is not of rank 2 or its value is larger than color | |
| map maximum entry. | |
| """ | |
| if label.ndim != 2: | |
| raise ValueError("Expect 2-D input label") | |
| colormap = np.asarray(ade_palette()) | |
| 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 | |
| palette = np.array(ade_palette()) | |
| for label, color in enumerate(palette): | |
| 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"]) | |
| demo.launch() |