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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 | |
del H1 | |
del H2 | |
del W | |
del W1 | |
del W2 | |
del h | |
del 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 | |
del name | |
del path | |
del transformed | |
del 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 | |
del img | |
del nms_prediction | |
del 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 | |
del name | |
del path | |
del 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 |