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Automated cropping of larvae from 4x Acquifer images
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| import os | ||
| import numpy as np | ||
| import argparse | ||
| import tifffile as tf | ||
| from skimage.measure import regionprops, label | ||
| import warnings | ||
| import re | ||
| from collections import defaultdict | ||
| from tqdm import tqdm | ||
| warnings.filterwarnings("ignore") | ||
| import pyclesperanto_prototype as cle | ||
|
|
||
| def parse_args(): | ||
| parser = argparse.ArgumentParser(description='Extract elongated ROIs from Acquifer TIF files and save them as multi-color TIF files.') | ||
| parser.add_argument('--input', type=str, help='Path to the folder containing the TIF files.') | ||
| parser.add_argument('--padding', type=int, default=50, help='Padding around the ROI (default: 50)') | ||
| return parser.parse_args() | ||
|
|
||
| def safe_divide(a, b, default=0): | ||
| return a / b if b != 0 else default | ||
|
|
||
| def get_roi(image, padding): | ||
| if image is None or image.size == 0: | ||
| return (0, 0, 0, 0) # Return a default ROI if the image is None or empty | ||
|
|
||
| # Normalize and convert to uint8 | ||
| image_min, image_max = np.min(image), np.max(image) | ||
| if image_max > image_min: | ||
| image = ((image - image_min) / (image_max - image_min) * 255).astype(np.uint8) | ||
| else: | ||
| image = np.zeros_like(image, dtype=np.uint8) # All black image if min == max | ||
|
|
||
| image1_gbp = cle.gaussian_blur(image, None, 10.0, 10.0, 0.0) | ||
| image3_vsp = cle.variance_sphere(image1_gbp, None, 1.0, 1.0, 0.0) | ||
| labels = cle.threshold_otsu(image3_vsp) | ||
|
|
||
| # Get ROIs | ||
| props = regionprops(labels, intensity_image=image) | ||
| y0, x0, y1, x1 = props[0].bbox | ||
| minr, minc = max(0, y0 - padding), max(0, x0 - padding) | ||
| maxr, maxc = min(labels.shape[0], y1 + padding), min(labels.shape[1], x1 + padding) | ||
|
|
||
| return (minr, minc, maxr - minr, maxc - minc) | ||
|
|
||
| def group_files(input_folder): | ||
| pattern = r'^-([A-Z]\d+)--PO(\d+)' | ||
| """ | ||
| This pattern captures all files that correspond | ||
| to the same well and PO (position) | ||
| cf. https://www.acquifer.de/resources/metadata/ | ||
| This means that all groups will be cropped based | ||
| on the ROI (+padding) of the first CO1 (brightfield) | ||
| file found in the group. | ||
| """ | ||
| files_grouped_by_well_and_position = defaultdict(list) | ||
|
|
||
| for filename in os.listdir(input_folder): | ||
| if filename.endswith('.tif'): | ||
| match = re.match(pattern, filename) | ||
| if match: | ||
| key = f"{match.group(1)}--PO{match.group(2)}" | ||
| files_grouped_by_well_and_position[key].append(filename) | ||
|
|
||
| return files_grouped_by_well_and_position | ||
|
|
||
| def process_files(input_folder, output_dir, padding): | ||
| files_grouped_by_well_and_position = group_files(input_folder) | ||
| print(f"Found {len(files_grouped_by_well_and_position)} groups of files.") | ||
|
|
||
| for group_key, files in tqdm(files_grouped_by_well_and_position.items(), desc="Processing file groups"): | ||
| try: | ||
| # Find the CO1 file in the group | ||
| co1_file = next((f for f in files if '--CO1--' in f), None) | ||
| if not co1_file: | ||
| print(f"No CO1 file found for group {group_key}. Skipping.") | ||
| continue | ||
|
|
||
| # Load CO1 image and get ROI | ||
| with tf.TiffFile(os.path.join(input_folder, co1_file)) as tif: | ||
| co1_image = tif.asarray() | ||
| roi = get_roi(co1_image, padding) | ||
|
|
||
| # Process all files in the group | ||
| for file in files: | ||
| with tf.TiffFile(os.path.join(input_folder, file)) as tif: | ||
| channel_image = tif.asarray() | ||
|
|
||
| # Crop and normalize | ||
| y, x, h, w = roi | ||
| cropped_image = channel_image[y:y+h, x:x+w] | ||
| normalized_image = ((cropped_image - cropped_image.min()) / (cropped_image.max() - cropped_image.min()) * 65535).astype(np.uint16) | ||
|
|
||
| # Save cropped image | ||
| output_filename = f"{os.path.splitext(file)[0]}_cropped.tif" | ||
| output_path = os.path.join(output_dir, output_filename) | ||
| tf.imwrite(output_path, normalized_image, compression='zlib') | ||
| print(f"Saved: {output_filename}") | ||
|
|
||
| except Exception as e: | ||
| print(f"Error processing group {group_key}: {str(e)}") | ||
|
|
||
| def main(): | ||
| args = parse_args() | ||
| input_folder = args.input | ||
| padding = args.padding | ||
| output_dir = os.path.join(input_folder, "processed_tifs") | ||
|
|
||
| if not os.path.exists(output_dir): | ||
| os.makedirs(output_dir) | ||
|
|
||
| process_files(input_folder, output_dir, padding) | ||
|
|
||
| if __name__ == "__main__": | ||
| main() |
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