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| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation | |
| model_checkpoint = "apple/deeplabv3-mobilevit-small" | |
| feature_extractor = MobileViTFeatureExtractor.from_pretrained(model_checkpoint) #, do_center_crop=False, size=(512, 512)) | |
| model = MobileViTForSemanticSegmentation.from_pretrained(model_checkpoint).eval() | |
| palette = np.array( | |
| [ | |
| [ 0, 0, 0], [192, 0, 0], [ 0, 192, 0], [192, 192, 0], | |
| [ 0, 0, 192], [192, 0, 192], [ 0, 192, 192], [192, 192, 192], | |
| [128, 0, 0], [255, 0, 0], [128, 192, 0], [255, 192, 0], | |
| [128, 0, 192], [255, 0, 192], [128, 192, 192], [255, 192, 192], | |
| [ 0, 128, 0], [192, 128, 0], [ 0, 255, 0], [192, 255, 0], | |
| [ 0, 128, 192] | |
| ], | |
| dtype=np.uint8) | |
| def predict(image): | |
| with torch.no_grad(): | |
| inputs = feature_extractor(image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| # Get preprocessed image. The pixel values don't need to be unnormalized | |
| # for this particular model. | |
| resized = (inputs["pixel_values"].numpy().squeeze().transpose(1, 2, 0)[..., ::-1] * 255).astype(np.uint8) | |
| # Class predictions for each pixel. | |
| classes = outputs.logits.argmax(1).squeeze().numpy().astype(np.uint8) | |
| # Super slow method but it works | |
| colored = np.zeros((classes.shape[0], classes.shape[1], 3), dtype=np.uint8) | |
| for y in range(classes.shape[0]): | |
| for x in range(classes.shape[1]): | |
| colored[y, x] = palette[classes[y, x]] | |
| # Resize predictions to input size (not original size). | |
| colored = Image.fromarray(colored) | |
| colored = colored.resize((resized.shape[1], resized.shape[0]), resample=Image.NEAREST) | |
| # Keep everything that is not background. | |
| mask = (classes != 0) * 255 | |
| mask = Image.fromarray(mask.astype(np.uint8)).convert("RGB") | |
| mask = mask.resize((resized.shape[1], resized.shape[0]), resample=Image.NEAREST) | |
| # Blend with the input image. | |
| resized = Image.fromarray(resized) | |
| highlighted = Image.blend(resized, mask, 0.4) | |
| return colored, highlighted | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.inputs.Image(label="Upload image"), | |
| outputs=[gr.outputs.Image(label="Classes"), gr.outputs.Image(label="Highlighted")], | |
| title="Semantic Segmentation with MobileViT and DeepLabV3", | |
| ).launch() | |
| # TODO: combo box with some example images | |