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| import numpy as np | |
| import tensorflow as tf | |
| import streamlit as st | |
| from PIL import Image | |
| 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.outputs.Image('pil'), gr.outputs.Image('pil')] | |
| st.header("Segmentacion de partes del cuerpo humano") | |
| st.markdown("Sube una imagen o selecciona un ejemplo para segmentar las distintas partes del cuerpo humano") | |
| file_imagen = st.file_uploader("Sube aqui tu imagen", type=["png", "jpg", "jpeg"]) | |
| examples = ["example_image_1.jpg", "example_image_2.jpg", "example_image_3.jpg"] | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| ex1 = Image.open(examples[0]) | |
| st.image(ex1, width=200) | |
| if st.button("Corre ejemplo 1"): | |
| file_imagen = examples[0] | |
| with col2: | |
| ex2 = Image.open(examples[1]) | |
| st.image(ex2, width=200) | |
| if st.button("Corre ejemplo 2"): | |
| file_imagen = examples[1] | |
| with col3: | |
| ex3 = Image.open(examples[2]) | |
| st.image(ex3, width=200) | |
| if st.button("Corre ejemplo 3"): | |
| file_imagen = examples[2] | |
| if file_imagen is not None: | |
| img = Image.open(file_imagen) | |
| output = segmentation(img) | |
| if output is not None: | |
| st.subheader("Segmentacion: ") | |
| st.write(output.shape) | |
| st.image(output[0], width=850) | |
| st.subheader("Mask: ") | |
| st.write(output.shape) | |
| st.image(output[1], width=850) | |
| # article = "<div style='text-align: center;'><a href='https://keras.io/examples/vision/deeplabv3_plus/' target='_blank'>Keras example by Praveen Kaushik</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) | |