def test1():
for x, target, img_path in dataset:
print(x.shape, target, img_path)
x = x.numpy()
x = x.transpose(1, 2, 0)[:, :, ::-1] # to (H, W, C), RGB to BGR,
x = np.ascontiguousarray(x)
h, w = x.shape[:2]
boxes = target[:, 2:].numpy()
labels = target[:, 1].numpy()
boxes = cxcywh2ltrb(boxes)
boxes[:, 0::2] *= w # lr
boxes[:, 1::2] *= h # tb
draw_target_in_image(x, boxes, labels, None, "voc")
cv.imshow("1", x)
cv.waitKey(0)
def test_detect():
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = YOLOv5(80).to(device)
print(load_params(model, "weights/yolov5s_coco.pth", strict=False))
dataset = LoadImages("./images", 640, 32)
model.float().fuse().eval().requires_grad_(False)
half = (device.type != 'cpu')
# half = False
model.half() if half else None
sum_t = 0.
# 预处理
x, img0, path = dataset[0]
x = x.to(device)
x = x.half() if half else x.float()
x /= 255
x = x[None] if x.dim() == 3 else x
model_info(model, x.shape[-2:])
for i in range(200):
t = time.time()
target = model(x)[0]
target = nms(target, 0.2, 0.45)
if i >= 5:
sum_t += time.time() - t
# 后处理
target = target[0]
convert_boxes(target, x.shape[-2:], img0.shape[:2])
boxes = target[:, :4].cpu().numpy()
scores = target[:, 4].cpu().numpy()
labels = target[:, 5].cpu().numpy()
img = img0.copy()
# 画图
draw_target_in_image(img, boxes, labels, scores, "coco")
img = resize_max(img, 720, 1080)
cv.imshow("1", img)
cv.waitKey(0)
print(sum_t / (200 - 5))
int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))
assert cap.isOpened()
fps = cap.get(cv.CAP_PROP_FPS)
frame_num = int(cap.get(cv.CAP_PROP_FRAME_COUNT))
print("视频帧率: %s" % fps)
model = efficientdet_d0(True).to(device)
model.eval()
for i in range(frame_num):
# 一帧一帧的捕获 跳着读
ret, image_o = cap.read() # BGR
if ret is False:
break # 未读到
image = cv_to_pil(image_o) # -> PIL.Image
image = trans.ToTensor()(image).to(
device) # -> tensor. shape(3, H, W), 0-1
start = time.time()
with torch.no_grad():
target = model([image],
image_size=max(image.shape),
score_thresh=score_thresh,
nms_thresh=nms_thresh)[0]
print("\r>> %d / %d. 处理时间: %f" % (i + 1, frame_num, time.time() - start),
end="",
flush=True)
new_image = draw_target_in_image(image_o, target)
out.write(new_image)
print()
cap.release()
out.release()
print("视频帧率: %s" % fps)
model = yolov3(True).to(device)
model.eval()
for i in range(frame_num):
# 一帧一帧的捕获 跳着读
ret, image_o = cap.read() # BGR
if ret is False:
break # 未读到
image = cv_to_pil(image_o) # -> PIL.Image
image = trans.ToTensor()(image).to(
device) # -> tensor. shape(3, H, W), 0-1
start = time.time()
with torch.no_grad():
target = model(
[image], # image_size=max(image.shape),
score_thresh=score_thresh,
nms_thresh=nms_thresh)[0]
print("\r>> %d / %d. 处理时间: %f" % (i + 1, frame_num, time.time() - start),
end="",
flush=True)
draw_target_in_image(image_o, target)
image_show = resize_max(image_o, 720, 1080)
cv.imshow("video", image_show)
if cv.waitKey(1) in (ord('q'), ord('Q')):
exit(0)
out.write(image_o)
print()
cap.release()
out.release()
def test_test():
img_path_list, target_list = \
make_dataset(r"D:\datasets\VOCdevkit\VOC0712\JPEGImages",
r"D:\datasets\VOCdevkit\VOC0712\Annotations",
r"D:\datasets\VOCdevkit\VOC0712\pkl\voc_0712_test.pkl",
r"D:\datasets\VOCdevkit\VOC0712\ImageSets\Main\test.txt", "voc", False)
dataset = LoadImagesAndLabels(img_path_list, target_list, 640, 32, 0.5,
False, {"batch_size": 1})
# x: Tensor[C, H, W], target_out: Tensor[X, 6], path: str. [idx, cls, *xywh]
# test show
def test1():
for x, target, img_path in dataset:
print(x.shape, target, img_path)
x = x.numpy()
x = x.transpose(1, 2, 0)[:, :, ::-1] # to (H, W, C), RGB to BGR,
x = np.ascontiguousarray(x)
h, w = x.shape[:2]
boxes = target[:, 2:].numpy()
labels = target[:, 1].numpy()
boxes = cxcywh2ltrb(boxes)
boxes[:, 0::2] *= w # lr
boxes[:, 1::2] *= h # tb
draw_target_in_image(x, boxes, labels, None, "voc")
cv.imshow("1", x)
cv.waitKey(0)
# test1()
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = YOLOv5(20).to(device)
print(load_params(model, "weights/yolov5s_voc.pth", strict=False))
model.float().fuse().eval().requires_grad_(False)
half = (device.type != 'cpu')
model.half() if half else None
hyp = {
"obj_pw": 0.911,
"cls_pw": 0.631,
"anchor_t": 2.91,
"box_lw": 0.0296,
"obj_lw": 0.301,
"cls_lw": 0.06075
}
loss_func = Loss(model, hyp)
loss = torch.zeros((4, ), device=device)
for x, target0, img_path in reversed(dataset):
img0 = x.numpy()
img0 = img0.transpose(1, 2, 0)[:, :, ::-1] # to (H, W, C), RGB to BGR,
img0 = np.ascontiguousarray(img0)
img = img0.copy()
# 预处理
x, target0 = x.to(device), target0.to(device)
x = x.half() if half else x.float()
x /= 255
if x.dim() == 3:
x = x[None]
# 预测
target, loss_target = model(x)
loss += loss_func([loss_t.float() for loss_t in loss_target],
target0)[1]
target = nms(target, 0.001, 0.6)
# 后处理
# 1
target = target[0]
boxes = target[:, :4].cpu().numpy()
scores = target[:, 4].cpu().numpy()
labels = target[:, 5].cpu().numpy()
draw_target_in_image(img, boxes, labels, scores, "voc")
cv.imshow("pred", img)
# 2
img2 = img0.copy()
h, w = img2.shape[:2]
boxes = target0[:, 2:].cpu().numpy()
labels = target0[:, 1].cpu().numpy()
boxes = cxcywh2ltrb(boxes)
boxes[:, 0::2] *= w # lr
boxes[:, 1::2] *= h # tb
draw_target_in_image(img2, boxes, labels, None, "voc")
cv.imshow("target", img2)
cv.waitKey(0)