52 lines
1.8 KiB
Python
Executable File
52 lines
1.8 KiB
Python
Executable File
#!/usr/bin/env python3
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import numpy as np
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import tensorflow as tf
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from PIL import Image, ImageOps
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from pathlib import Path
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SEG_MODEL_FILE_PATH = "../../app/build/downloads/fairscan-segmentation-model.tflite"
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DATASET_DIR = Path("../dataset")
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INPUT_WIDTH = 256
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INPUT_HEIGHT = 256
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def get_resized_image(image_path):
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img = Image.open(image_path).convert("RGB")
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img = ImageOps.exif_transpose(img)
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img = img.convert("RGB").resize((INPUT_WIDTH, INPUT_HEIGHT), Image.BILINEAR)
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return img
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def preprocess_image(img: Image.Image) -> np.ndarray:
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img_np = np.asarray(img).astype(np.float32)
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img_np = (img_np - 127.5) / 127.5 # Normalize to [-1, 1]
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return np.expand_dims(img_np, axis=0)
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def postprocess_output(output: np.ndarray) -> np.ndarray:
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output = np.squeeze(output).astype(np.float32) # Shape: (256, 256)
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output = np.clip(output, 0, 1)
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return output # float32 array, values in [0,1]
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def get_segmentation_mask(img):
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input_tensor = preprocess_image(img)
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interpreter.set_tensor(input_details['index'], input_tensor)
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interpreter.invoke()
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output_tensor = interpreter.get_tensor(output_details['index'])
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return postprocess_output(output_tensor)
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interpreter = tf.lite.Interpreter(model_path=str(SEG_MODEL_FILE_PATH))
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()[0]
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output_details = interpreter.get_output_details()[0]
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img_input_dir = DATASET_DIR / "images"
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mask_input_dir = DATASET_DIR / "masks"
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for image_path in sorted(img_input_dir.glob("*.jpg")):
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print(f"Generating mask for {image_path}")
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img = get_resized_image(image_path)
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img = ImageOps.exif_transpose(img)
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mask = get_segmentation_mask(img)
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mask_path = mask_input_dir / (image_path.stem + ".png")
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Image.fromarray((mask * 255).astype(np.uint8)).save(mask_path)
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