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| import argparse | |
| import os | |
| from enum import Enum | |
| import numpy as np | |
| import pandas as pd | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| from atoms_detection.dataset import CoordinatesDataset | |
| from utils.constants import Split, Catalyst, Method | |
| from utils.paths import DETECTION_LOGS, IMG_PATH, PRED_GT_VIS_PATH, PT_DATASET, FE_DATASET, DETECTION_PATH | |
| from visualizations.utils import plot_gt_pred_on_img | |
| def main(args): | |
| catalyst = args.catalyst | |
| method = args.method | |
| if not os.path.exists(PRED_GT_VIS_PATH): | |
| os.makedirs(PRED_GT_VIS_PATH) | |
| if catalyst == Catalyst.Pt: | |
| coordinates_dataset = CoordinatesDataset(PT_DATASET) | |
| if method == Method.DL: | |
| detection_path = "data/detection_data/dl_detection_sac_cnn/dl_detection_sac_cnn_0.89" | |
| elif method == Method.CV: | |
| detection_path = os.path.join(DETECTION_PATH, "cv_detection_trial_0.18") | |
| elif method == Method.TEM: | |
| detection_path = os.path.join(DETECTION_PATH, "tem_imagenet_pt", | |
| "tem_imagenet_pt_denoise-bg_Gen1GaussianMask") | |
| else: | |
| raise NotImplementedError | |
| elif catalyst == Catalyst.Fe: | |
| coordinates_dataset = CoordinatesDataset(FE_DATASET) | |
| if method == Method.DL: | |
| detection_path = os.path.join(DETECTION_PATH, f"dl_fe_detection_trial", | |
| f"dl_fe_detection_trial_0.97") | |
| elif method == Method.CV: | |
| detection_path = os.path.join(DETECTION_PATH, "cv_fe_detection_trial", | |
| "cv_fe_detection_trial_0.21") | |
| elif method == Method.TEM: | |
| detection_path = os.path.join(DETECTION_PATH, "tem_imagenet_fe", | |
| "tem_imagenet_fe_denoise-bg_Gen1GaussianMask") | |
| else: | |
| raise NotImplementedError | |
| else: | |
| raise NotImplementedError | |
| gt_coords_dict = get_gt_coords(coordinates_dataset) | |
| for name_file in os.listdir(detection_path): | |
| image_name = os.path.splitext(name_file)[0] + ".tif" | |
| print(image_name) | |
| if image_name not in gt_coords_dict: | |
| continue | |
| filepath = os.path.join(detection_path, name_file) | |
| image_filename = os.path.join(IMG_PATH, image_name) | |
| img = Image.open(image_filename) | |
| gt_coords = gt_coords_dict[image_name] | |
| df_predicted = pd.read_csv(filepath) | |
| pred_coords = [(row['x'], row['y']) for _, row in df_predicted.iterrows()] | |
| img_arr = np.array(img).astype(np.float32) | |
| img_normed = (img_arr - img_arr.min()) / (img_arr.max() - img_arr.min()) | |
| plot_gt_pred_on_img(img_normed, gt_coords, pred_coords) | |
| vis_folder = os.path.join(PRED_GT_VIS_PATH, f"{catalyst}-Catalyst_{method}-Method") | |
| if not os.path.exists(vis_folder): | |
| os.makedirs(vis_folder) | |
| clean_image_name = os.path.splitext(image_name)[0] | |
| vis_path = os.path.join(vis_folder, f'{clean_image_name}.png') | |
| plt.savefig(vis_path, bbox_inches='tight', pad_inches=0.0, transparent=True) | |
| plt.close() | |
| def get_gt_coords(coordinates_dataset): | |
| gt_coords_dict = {} | |
| for image_path, coordinates_path in coordinates_dataset.iterate_data(Split.TEST): | |
| # orig . image_name = os.path.splitext(os.path.basename(image_path))[0] + ".tif" | |
| image_name = os.path.basename(image_path) | |
| gt_coords = coordinates_dataset.load_coordinates(coordinates_path) | |
| gt_coords_dict[image_name] = gt_coords | |
| return gt_coords_dict | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "catalyst", | |
| type=Catalyst, | |
| choices=Catalyst, | |
| help="Select data by catalyst" | |
| ) | |
| parser.add_argument( | |
| "method", | |
| type=Method, | |
| choices=Method, | |
| help="Select method" | |
| ) | |
| return parser.parse_args() | |
| if __name__ == "__main__": | |
| args = get_args() | |
| main(args) | |