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import argparse | |
import time | |
from pathlib import Path | |
import cv2 | |
import torch | |
import torch.backends.cudnn as cudnn | |
from numpy import random | |
from models.experimental import attempt_load | |
from utils.datasets import LoadStreams, LoadImages | |
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ | |
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path | |
from utils.plots import plot_one_box | |
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel | |
def detect(save_img=False): | |
source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace | |
save_img = not opt.nosave and not source.endswith('.txt') # save inference images | |
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( | |
('rtsp://', 'rtmp://', 'http://', 'https://')) | |
# Directories | |
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run | |
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir | |
# Initialize | |
set_logging() | |
device = select_device(opt.device) | |
half = device.type != 'cpu' # half precision only supported on CUDA | |
# Load model | |
model = attempt_load(weights, map_location=device) # load FP32 model | |
stride = int(model.stride.max()) # model stride | |
imgsz = check_img_size(imgsz, s=stride) # check img_size | |
if trace: | |
model = TracedModel(model, device, opt.img_size) | |
if half: | |
model.half() # to FP16 | |
# Second-stage classifier | |
classify = False | |
if classify: | |
modelc = load_classifier(name='resnet101', n=2) # initialize | |
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() | |
# Set Dataloader | |
vid_path, vid_writer = None, None | |
if webcam: | |
view_img = check_imshow() | |
cudnn.benchmark = True # set True to speed up constant image size inference | |
dataset = LoadStreams(source, img_size=imgsz, stride=stride) | |
else: | |
dataset = LoadImages(source, img_size=imgsz, stride=stride) | |
# Get names and colors | |
names = model.module.names if hasattr(model, 'module') else model.names | |
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] | |
# Run inference | |
if device.type != 'cpu': | |
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once | |
old_img_w = old_img_h = imgsz | |
old_img_b = 1 | |
t0 = time.time() | |
for path, img, im0s, vid_cap in dataset: | |
img = torch.from_numpy(img).to(device) | |
img = img.half() if half else img.float() # uint8 to fp16/32 | |
img /= 255.0 # 0 - 255 to 0.0 - 1.0 | |
if img.ndimension() == 3: | |
img = img.unsqueeze(0) | |
# Warmup | |
if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): | |
old_img_b = img.shape[0] | |
old_img_h = img.shape[2] | |
old_img_w = img.shape[3] | |
for i in range(3): | |
model(img, augment=opt.augment)[0] | |
# Inference | |
t1 = time_synchronized() | |
with torch.no_grad(): # Calculating gradients would cause a GPU memory leak | |
pred = model(img, augment=opt.augment)[0] | |
t2 = time_synchronized() | |
# Apply NMS | |
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) | |
t3 = time_synchronized() | |
# Apply Classifier | |
if classify: | |
pred = apply_classifier(pred, modelc, img, im0s) | |
# Process detections | |
for i, det in enumerate(pred): # detections per image | |
if webcam: # batch_size >= 1 | |
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count | |
else: | |
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) | |
p = Path(p) # to Path | |
save_path = str(save_dir / p.name) # img.jpg | |
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt | |
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh | |
if len(det): | |
# Rescale boxes from img_size to im0 size | |
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() | |
# Print results | |
for c in det[:, -1].unique(): | |
n = (det[:, -1] == c).sum() # detections per class | |
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string | |
# Write results | |
for *xyxy, conf, cls in reversed(det): | |
if save_txt: # Write to file | |
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh | |
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format | |
with open(txt_path + '.txt', 'a') as f: | |
f.write(('%g ' * len(line)).rstrip() % line + '\n') | |
if save_img or view_img: # Add bbox to image | |
label = f'{names[int(cls)]} {conf:.2f}' | |
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) | |
# Print time (inference + NMS) | |
print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') | |
# Stream results | |
if view_img: | |
cv2.imshow(str(p), im0) | |
cv2.waitKey(1) # 1 millisecond | |
# Save results (image with detections) | |
if save_img: | |
if dataset.mode == 'image': | |
cv2.imwrite(save_path, im0) | |
print(f" The image with the result is saved in: {save_path}") | |
else: # 'video' or 'stream' | |
if vid_path != save_path: # new video | |
vid_path = save_path | |
if isinstance(vid_writer, cv2.VideoWriter): | |
vid_writer.release() # release previous video writer | |
if vid_cap: # video | |
fps = vid_cap.get(cv2.CAP_PROP_FPS) | |
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
else: # stream | |
fps, w, h = 30, im0.shape[1], im0.shape[0] | |
save_path += '.mp4' | |
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) | |
vid_writer.write(im0) | |
if save_txt or save_img: | |
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' | |
#print(f"Results saved to {save_dir}{s}") | |
print(f'Done. ({time.time() - t0:.3f}s)') |