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| import numpy as np | |
| import tensorflow as tf | |
| import gradio as gr | |
| from huggingface_hub import from_pretrained_keras | |
| import cv2 | |
| model = from_pretrained_keras("keras-io/deeplabv3p-resnet50") | |
| colormap = np.array([[0,0,0], [31,119,180], [44,160,44], [44, 127, 125], [52, 225, 143], | |
| [217, 222, 163], [254, 128, 37], [130, 162, 128], [121, 7, 166], [136, 183, 248], | |
| [85, 1, 76], [22, 23, 62], [159, 50, 15], [101, 93, 152], [252, 229, 92], | |
| [167, 173, 17], [218, 252, 252], [238, 126, 197], [116, 157, 140], [214, 220, 252]], dtype=np.uint8) | |
| img_size = 512 | |
| def read_image(image): | |
| image = tf.convert_to_tensor(image) | |
| image.set_shape([None, None, 3]) | |
| image = tf.image.resize(images=image, size=[img_size, img_size]) | |
| image = image / 127.5 - 1 | |
| return image | |
| def infer(model, image_tensor): | |
| predictions = model.predict(np.expand_dims((image_tensor), axis=0)) | |
| predictions = np.squeeze(predictions) | |
| predictions = np.argmax(predictions, axis=2) | |
| return predictions | |
| def decode_segmentation_masks(mask, colormap, n_classes): | |
| r = np.zeros_like(mask).astype(np.uint8) | |
| g = np.zeros_like(mask).astype(np.uint8) | |
| b = np.zeros_like(mask).astype(np.uint8) | |
| for l in range(0, n_classes): | |
| idx = mask == l | |
| r[idx] = colormap[l, 0] | |
| g[idx] = colormap[l, 1] | |
| b[idx] = colormap[l, 2] | |
| rgb = np.stack([r, g, b], axis=2) | |
| return rgb | |
| def get_overlay(image, colored_mask): | |
| image = tf.keras.preprocessing.image.array_to_img(image) | |
| image = np.array(image).astype(np.uint8) | |
| overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0) | |
| return overlay | |
| def segmentation(input_image): | |
| image_tensor = read_image(input_image) | |
| prediction_mask = infer(image_tensor=image_tensor, model=model) | |
| prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20) | |
| overlay = get_overlay(image_tensor, prediction_colormap) | |
| return (overlay, prediction_colormap) | |
| i = gr.inputs.Image() | |
| o = [gr.Image(), gr.Image()] | |
| examples = ["example_image_1.jpg", "example_image_2.jpeg", "example_image_3.jpeg"] | |
| title = "Human Part Segmentation" | |
| description = "Upload an image or select from examples to segment out different human parts." | |
| article = "<div style='text-align: center;'><a href='https://twitter.com/SatpalPatawat' target='_blank'>Space by Satpal Singh Rathore</a><br><a href='https://keras.io/examples/vision/deeplabv3_plus/' target='_blank'>Keras example by Soumik Rakshit</a></div>" | |
| gr.Interface(segmentation, i, o, examples=examples, allow_flagging=False, analytics_enabled=False, | |
| title=title, description=description, article=article).launch(enable_queue=True) |