Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2020-02-22 18:18:38 -08:00
parent 7608047531
commit bc741f30e8
2 changed files with 13 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
detect.py
View File

@ -64,10 +64,10 @@ def detect(save_img=False):
if webcam: if webcam:
view_img = True view_img = True
torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=img_size, half=half) dataset = LoadStreams(source, img_size=img_size)
else: else:
save_img = True save_img = True
dataset = LoadImages(source, img_size=img_size, half=half) dataset = LoadImages(source, img_size=img_size)
# Get names and colors # Get names and colors
names = load_classes(opt.names) names = load_classes(opt.names)
@ -77,15 +77,14 @@ def detect(save_img=False):
t0 = time.time() t0 = time.time()
for path, img, im0s, vid_cap in dataset: for path, img, im0s, vid_cap in dataset:
t = time.time() t = time.time()
# Get detections
img = torch.from_numpy(img).to(device) 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: if img.ndimension() == 3:
img = img.unsqueeze(0) img = img.unsqueeze(0)
pred = model(img)[0]
if opt.half: # Inference
pred = pred.float() pred = model(img)[0].float() if half else model(img)[0]
# Apply NMS # Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)

20
utils/datasets.py
View File

@ -42,7 +42,7 @@ def exif_size(img):
class LoadImages: # for inference class LoadImages: # for inference
def __init__(self, path, img_size=416, half=False): def __init__(self, path, img_size=416):
path = str(Path(path)) # os-agnostic path = str(Path(path)) # os-agnostic
files = [] files = []
if os.path.isdir(path): if os.path.isdir(path):
@ -59,7 +59,6 @@ class LoadImages: # for inference
self.nF = nI + nV # number of files self.nF = nI + nV # number of files
self.video_flag = [False] * nI + [True] * nV self.video_flag = [False] * nI + [True] * nV
self.mode = 'images' self.mode = 'images'
self.half = half # half precision fp16 images
if any(videos): if any(videos):
self.new_video(videos[0]) # new video self.new_video(videos[0]) # new video
else: else:
@ -104,8 +103,7 @@ class LoadImages: # for inference
# Convert # Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32 img = np.ascontiguousarray(img)
img /= 255.0 # 0 - 255 to 0.0 - 1.0
# cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image # cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
return path, img, img0, self.cap return path, img, img0, self.cap
@ -120,9 +118,8 @@ class LoadImages: # for inference
class LoadWebcam: # for inference class LoadWebcam: # for inference
def __init__(self, pipe=0, img_size=416, half=False): def __init__(self, pipe=0, img_size=416):
self.img_size = img_size self.img_size = img_size
self.half = half # half precision fp16 images
if pipe == '0': if pipe == '0':
pipe = 0 # local camera pipe = 0 # local camera
@ -177,8 +174,7 @@ class LoadWebcam: # for inference
# Convert # Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32 img = np.ascontiguousarray(img)
img /= 255.0 # 0 - 255 to 0.0 - 1.0
return img_path, img, img0, None return img_path, img, img0, None
@ -187,10 +183,9 @@ class LoadWebcam: # for inference
class LoadStreams: # multiple IP or RTSP cameras class LoadStreams: # multiple IP or RTSP cameras
def __init__(self, sources='streams.txt', img_size=416, half=False): def __init__(self, sources='streams.txt', img_size=416):
self.mode = 'images' self.mode = 'images'
self.img_size = img_size self.img_size = img_size
self.half = half # half precision fp16 images
if os.path.isfile(sources): if os.path.isfile(sources):
with open(sources, 'r') as f: with open(sources, 'r') as f:
@ -251,9 +246,8 @@ class LoadStreams: # multiple IP or RTSP cameras
img = np.stack(img, 0) img = np.stack(img, 0)
# Convert # Convert
img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to 3x416x416, uint8 to float32 img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416
img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) img = np.ascontiguousarray(img)
img /= 255.0 # 0 - 255 to 0.0 - 1.0
return self.sources, img, img0, None return self.sources, img, img0, None