New module: evaluation (#78)

evaluation/python/generate_masks.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import tensorflow as tf
+from PIL import Image, ImageOps
+from pathlib import Path
+
+SEG_MODEL_FILE_PATH = "../../app/build/downloads/fairscan-segmentation-model.tflite"
+DATASET_DIR = Path("../dataset")
+
+INPUT_WIDTH = 256
+INPUT_HEIGHT = 256
+
+def get_resized_image(image_path):
+    img = Image.open(image_path).convert("RGB")
+    img = ImageOps.exif_transpose(img)
+    img = img.convert("RGB").resize((INPUT_WIDTH, INPUT_HEIGHT), Image.BILINEAR)
+    return img
+
+def preprocess_image(img: Image.Image) -> np.ndarray:
+    img_np = np.asarray(img).astype(np.float32)
+    img_np = (img_np - 127.5) / 127.5  # Normalize to [-1, 1]
+    return np.expand_dims(img_np, axis=0)
+
+def postprocess_output(output: np.ndarray) -> np.ndarray:
+    output = np.squeeze(output).astype(np.float32)  # Shape: (256, 256)
+    output = np.clip(output, 0, 1)
+    return output  # float32 array, values in [0,1]
+
+def get_segmentation_mask(img):
+    input_tensor = preprocess_image(img)
+    interpreter.set_tensor(input_details['index'], input_tensor)
+    interpreter.invoke()
+    output_tensor = interpreter.get_tensor(output_details['index'])
+    return postprocess_output(output_tensor)
+
+interpreter = tf.lite.Interpreter(model_path=str(SEG_MODEL_FILE_PATH))
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()[0]
+output_details = interpreter.get_output_details()[0]
+
+img_input_dir = DATASET_DIR / "images"
+mask_input_dir = DATASET_DIR / "masks"
+
+for image_path in sorted(img_input_dir.glob("*.jpg")):
+    print(f"Generating mask for {image_path}")
+    img = get_resized_image(image_path)
+    img = ImageOps.exif_transpose(img)
+    mask = get_segmentation_mask(img)
+    mask_path = mask_input_dir / (image_path.stem + ".png")
+    Image.fromarray((mask * 255).astype(np.uint8)).save(mask_path)

evaluation/python/requirements.txt

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+tensorflow
+numpy
+Pillow