104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
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import numpy as np
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from pathlib import Path
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from skimage.draw import polygon
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from tifffile import TiffFile
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from src.database.db_manager import DatabaseManager
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def read_image(image_path: Path) -> np.ndarray:
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metadata = {}
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with TiffFile(image_path) as tif:
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image = tif.asarray()
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metadata = tif.imagej_metadata
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return image, metadata
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def main():
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polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
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image = np.zeros((100, 100), dtype=np.uint8)
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rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
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image[rr, cc] = 255
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if __name__ == "__main__":
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db = DatabaseManager()
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model_name = "c17"
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model_id = db.get_models(filters={"model_name": model_name})[0]["id"]
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print(f"Model name {model_name}, id {model_id}")
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detections = db.get_detections(filters={"model_id": model_id})
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file_stems = set()
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for detection in detections:
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file_stems.add(detection["image_filename"].split("_")[0])
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print("Files:", file_stems)
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for stem in file_stems:
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print(stem)
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detections = db.get_detections(filters={"model_id": model_id, "i.filename": f"LIKE %{stem}%"})
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annotations = []
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for detection in detections:
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source_path = Path(detection["metadata"]["source_path"])
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image, metadata = read_image(source_path)
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offset = np.array(list(map(int, metadata["tile_section"].split(","))))[::-1]
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scale = np.array(list(map(int, metadata["patch_size"].split(","))))[::-1]
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# tile_size = np.array(list(map(int, metadata["tile_size"].split(","))))
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segmentation = np.array(detection["segmentation_mask"]) # * tile_size
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# print(source_path, image, metadata, segmentation.shape)
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# print(offset)
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# print(scale)
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# print(segmentation)
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# segmentation = (segmentation + offset * tile_size) / (tile_size * scale)
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segmentation = (segmentation + offset) / scale
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yolo_annotation = f"{detection['metadata']['class_id']} " + " ".join(
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[f"{x:.6f} {y:.6f}" for x, y in segmentation]
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)
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annotations.append(yolo_annotation)
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# print(segmentation)
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# print(yolo_annotation)
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# aa
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print(
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" ",
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detection["model_name"],
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detection["image_id"],
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detection["image_filename"],
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source_path,
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metadata["label_path"],
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)
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# section_i_section_j = detection["image_filename"].split("_")[1].split(".")[0]
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# print(" ", section_i_section_j)
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label_path = metadata["label_path"]
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print(" ", label_path)
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with open(label_path, "w") as f:
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f.write("\n".join(annotations))
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exit()
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for detection in detections:
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print(detection["model_name"], detection["image_id"], detection["image_filename"])
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print(detections[0])
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# polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
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# image = np.zeros((100, 100), dtype=np.uint8)
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# rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
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# image[rr, cc] = 255
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# import matplotlib.pyplot as plt
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# plt.imshow(image, cmap='gray')
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# plt.show()
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