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generate_starfile_new_data_jpg.py
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generate_starfile_new_data_jpg.py
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# Code for generating star file
from utils.denoise import denoise, denoise_jpg_image
import config
import matplotlib.pyplot as plt
import numpy as np
import torch
import cv2
import csv
import glob
import random
from dataset.dataset import transform, min_max
from models.model_5_layers import UNET
import config
from tqdm import tqdm
import mrcfile
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import statistics as st
import config
print("[INFO] Loading up model...")
model = UNET().to(device=config.device)
state_dict = torch.load(config.cryosegnet_checkpoint)
model.load_state_dict(state_dict)
sam_model = sam_model_registry[config.model_type](checkpoint=config.sam_checkpoint)
sam_model.to(device=config.device)
mask_generator = SamAutomaticMaskGenerator(sam_model)
def get_annotations(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
img[:,:,3] = 0
for ann in sorted_anns:
m = ann['segmentation']
color_mask = np.concatenate([np.random.random(3), [0.35]])
img[m] = color_mask
return img
def generate_output(model, image_path, star_writer):
# set model to evaluation mode
model.eval()
# turn off gradient tracking
with torch.no_grad():
image = cv2.imread(image_path, 0)
#Check if denoising makes difference or not! If the images are already denoised don't denoise them else denoise them!
image = denoise_jpg_image(image)
height, width = image.shape
image = cv2.resize(image, (config.input_image_width, config.input_image_height))
image = torch.from_numpy(image).unsqueeze(0).float()
image = image / 255.0
image = image.to(config.device).unsqueeze(0)
predicted_mask = model(image)
predicted_mask = torch.sigmoid(predicted_mask)
predicted_mask = predicted_mask.cpu().numpy().reshape(config.input_image_width, config.input_image_height)
# Uncomment these two lines if you find positions of predicted proteins are flipped
#predicted_mask = np.rot90(predicted_mask, k=3)
#predicted_mask = predicted_mask.T
sam_output = np.repeat(transform(predicted_mask)[:,:,None], 3, axis=-1)
predicted_mask = cv2.resize(predicted_mask, (width, height))
predicted_mask = min_max(predicted_mask)
masks = mask_generator.generate(sam_output)
sam_mask = get_annotations(masks)
sam_mask = cv2.resize(sam_mask, (width, height) )
bboxes = {"bbox": [], "iou": []}
for i in range(0, len(masks)):
if masks[i]["predicted_iou"] > 0.94:
bboxes["bbox"].append(masks[i]["bbox"])
bboxes["iou"].append(masks[i]["predicted_iou"])
if len(bboxes) > 1:
x_ = st.mode([box[2] for box in bboxes["bbox"]])
y_ = st.mode([box[3] for box in bboxes["bbox"]])
d_ = np.sqrt((x_ * width / config.input_image_width)**2 + (y_ * height / config.input_image_height)**2)
r_ = int(d_//2)
th = r_ * 0.2
filename = image_path.split("/")[-1][:-4] + '.mrc'
for i in range(len(bboxes["bbox"])):
box, iou = bboxes["bbox"][i], bboxes["iou"][i]
if box[2] < x_ + th and box[2] > x_ - th/3 and box[3] < y_ + th and box[3] > y_ - th/3:
x_new, y_new = int((box[0] + box[2]/2) / config.input_image_width * width) , int((box[1] + box[3]/2) / config.input_image_height * height)
star_writer.writerow([filename, x_new, y_new, 2*r_])
if iou > 0.9999:
star_writer.writerow([filename, x_new + random.randint(-int(r_ / 10), int(r_ / 10)), y_new + random.randint(-int(r_ / 10), int(r_ / 10)), 2*r_])
else:
pass
print("[INFO] Loading up Test Micrographs ...")
images_path = list(glob.glob(f"{config.my_dataset_path}/*.*p*g"))
print(f"[INFO] Number of Micrographs = {len(images_path)}\n")
print("[INFO] Generating star file for input Cryo-EM Micrographs...")
print("[INFO] Generation may take more time depending upon the number of micrographs...\n")
with open(f"{config.output_path}/star_files/{config.file_name}", "w") as star_file:
star_writer = csv.writer(star_file, delimiter=' ')
star_writer.writerow([])
star_writer.writerow(["data_"])
star_writer.writerow([])
star_writer.writerow(["loop_"])
star_writer.writerow(["_rlnMicrographName", "#1"])
star_writer.writerow(["_rlnCoordinateX", "#2"])
star_writer.writerow(["_rlnCoordinateY", "#3"])
star_writer.writerow(["_rlnDiameter", "#4"])
for i in tqdm(range(0, len(images_path), 1)):
generate_output(model, images_path[i], star_writer)