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| import torch | |
| import cv2 | |
| import os | |
| import torch.nn as nn | |
| import numpy as np | |
| import torchvision | |
| from torchvision.ops import box_iou | |
| from PIL import Image | |
| import albumentations as A | |
| from albumentations.pytorch import ToTensorV2 | |
| import cv2 | |
| import tqdm | |
| import gc | |
| from time import sleep | |
| import shutil | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| # apply nms algorithm | |
| def apply_nms(orig_prediction, iou_thresh=0.3): | |
| # torchvision returns the indices of the bboxes to keep | |
| keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh) | |
| final_prediction = orig_prediction | |
| final_prediction['boxes'] = final_prediction['boxes'][keep] | |
| final_prediction['scores'] = final_prediction['scores'][keep] | |
| final_prediction['labels'] = final_prediction['labels'][keep] | |
| return final_prediction | |
| def apply_nms2(orig_prediction, iou_thresh=0.3): | |
| # torchvision returns the indices of the bboxes to keep | |
| preds = [] | |
| for prediction in orig_prediction: | |
| keep = torchvision.ops.nms(prediction['boxes'], prediction['scores'], iou_thresh) | |
| final_prediction = prediction | |
| final_prediction['boxes'] = final_prediction['boxes'][keep] | |
| final_prediction['scores'] = final_prediction['scores'][keep] | |
| final_prediction['labels'] = final_prediction['labels'][keep] | |
| preds.append(final_prediction) | |
| return preds | |
| # Draw the bounding box | |
| def plot_img_bbox(img, target): | |
| h,w,c = img.shape | |
| for box in (target['boxes']): | |
| xmin, ymin, xmax, ymax = int((box[0].cpu()/1024)*w), int((box[1].cpu()/1024)*h), int((box[2].cpu()/1024)*w),int((box[3].cpu()/1024)*h) | |
| cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) | |
| label = "palm" | |
| # Add the label and confidence score | |
| label = f'{label}' | |
| cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) | |
| # Display the image with detections | |
| #filename = 'pred.jpg' | |
| #cv2.imwrite(filename, img) | |
| return img | |
| def crop(image,size=1024): | |
| #input = os.path.join(path,image) | |
| #img = cv2.imread(input) | |
| img = image.copy() | |
| H, W,_ = img.shape | |
| h = (H//size) | |
| w = (W//size) | |
| H1 = h*size | |
| W1 = w*size | |
| os.makedirs("images", exist_ok=True) | |
| images = [] | |
| #images_truth = [] | |
| locations = [] | |
| if H1 < H : | |
| chevauche_h = H-H1 | |
| rest_h = 1024-chevauche_h | |
| val_h = H1-rest_h | |
| H2 = [x for x in range(0,H1,size)] +[val_h] | |
| else : | |
| H2 = [x for x in range(0,H1,size)] | |
| if W1 <W : | |
| chevauche_w = W-W1 | |
| rest_w = 1024-chevauche_w | |
| val_w = W1-rest_w | |
| W2 = [x for x in range(0,W1,size)] +[val_w] | |
| else: | |
| W2 = [x for x in range(0,W1,size)] | |
| for i in H2: | |
| for j in W2: | |
| crop_img = img[i:i+size, j:j+size,:] | |
| name = "img_"+str(i)+"_"+str(j)+".png" | |
| ## csv file creation | |
| location = [i,i+size,j,j+size] | |
| locations.append(location) | |
| cv2.imwrite(os.path.join("images",name),crop_img) | |
| del crop_img | |
| gc.collect() | |
| #sleep(2) | |
| del H,H1,H2,W,W1,W2,h,w | |
| gc.collect() | |
| sleep(1) | |
| np.save("locations.npy",np.array(locations)) | |
| def inference(image,locations,model,test_transforms,device): | |
| n = 0 | |
| os.makedirs("labels", exist_ok=True) | |
| for i,location in enumerate(locations): | |
| name = "img_"+str(location[0])+"_"+str(location[2])+".png" | |
| path = os.path.join("images",name) | |
| imgs = np.array(cv2.imread(path)) | |
| transformed = test_transforms(image= imgs) | |
| image_transformed = transformed["image"] | |
| image_transformed = image_transformed.unsqueeze(0) | |
| image_transformed = image_transformed.to(device) | |
| model.eval() | |
| with torch.no_grad(): | |
| predictions = model(image_transformed) | |
| del imgs,name,path,transformed,image_transformed | |
| gc.collect() | |
| sleep(1) | |
| nms_prediction = apply_nms2(predictions, iou_thresh=0.1) | |
| img = image[location[0]:location[1],location[2]:location[3],:] | |
| n = n+len(nms_prediction[0]['boxes']) | |
| for box in (nms_prediction[0]['boxes']): | |
| xmin, ymin, xmax, ymax = int(box[0].cpu()), int(box[1].cpu()), int(box[2].cpu()),int(box[3].cpu()) | |
| cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2) | |
| label = "palm" | |
| # Add the label and confidence score | |
| label = f'{label}' | |
| cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) | |
| del label | |
| #empty_image[location[0]:location[1],location[2]:location[3],:] = img | |
| label_name = "lab_"+str(location[0])+"_"+str(location[2])+".png" | |
| cv2.imwrite(os.path.join("labels",label_name),img) | |
| del label_name,img,nms_prediction,predictions | |
| gc.collect() | |
| sleep(1) | |
| return n | |
| def create_new_ortho(locations,empty_image): | |
| for i,location in tqdm(enumerate(locations),total=len(locations)): | |
| name = "lab_"+str(location[0])+"_"+str(location[2])+".png" | |
| path = os.path.join("labels",name) | |
| img = np.array(cv2.imread(path)) | |
| empty_image[location[0]:location[1],location[2]:location[3],:] = img | |
| if i%300==0: | |
| cv2.imwrite("img.png",empty_image) | |
| del img,name,path,empty_image | |
| gc.collect() | |
| #sleep(1) | |
| empty_image = np.array(cv2.imread("img.png")) | |
| cv2.imwrite("img.png",empty_image) | |
| empty_image = np.array(cv2.imread("img.png")) | |
| return empty_image |