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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], | |
| ] | |
| 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') | |
| ade20k_labels_info = pd.read_csv( | |
| "https://raw.githubusercontent.com/CSAILVision/sceneparsing/master/objectInfo150.csv" | |
| ) | |
| labels_list = list(ade20k_labels_info["Name"]) | |
| 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() |