190 lines
8.1 KiB
Python
190 lines
8.1 KiB
Python
import argparse
|
||
|
||
from models import * # set ONNX_EXPORT in models.py
|
||
from utils.datasets import *
|
||
from utils.utils import *
|
||
|
||
|
||
def detect(save_img=False):
|
||
img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
|
||
out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
|
||
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
|
||
|
||
# Initialize
|
||
device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
|
||
if os.path.exists(out):
|
||
shutil.rmtree(out) # delete output folder
|
||
os.makedirs(out) # make new output folder
|
||
|
||
# Initialize model
|
||
model = Darknet(opt.cfg, img_size)
|
||
|
||
# Load weights
|
||
attempt_download(weights)
|
||
if weights.endswith('.pt'): # pytorch format
|
||
model.load_state_dict(torch.load(weights, map_location=device)['model'])
|
||
else: # darknet format
|
||
load_darknet_weights(model, weights)
|
||
|
||
# Second-stage classifier
|
||
classify = False
|
||
if classify:
|
||
modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
|
||
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
|
||
modelc.to(device).eval()
|
||
|
||
# Eval mode
|
||
model.to(device).eval()
|
||
|
||
# Fuse Conv2d + BatchNorm2d layers
|
||
# model.fuse()
|
||
|
||
# Export mode
|
||
if ONNX_EXPORT:
|
||
model.fuse()
|
||
img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192)
|
||
f = opt.weights.replace(opt.weights.split('.')[-1], 'onnx') # *.onnx filename
|
||
torch.onnx.export(model, img, f, verbose=False, opset_version=11,
|
||
input_names=['images'], output_names=['classes', 'boxes'])
|
||
|
||
# Validate exported model
|
||
import onnx
|
||
model = onnx.load(f) # Load the ONNX model
|
||
onnx.checker.check_model(model) # Check that the IR is well formed
|
||
print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph
|
||
return
|
||
|
||
# Half precision
|
||
half = half and device.type != 'cpu' # half precision only supported on CUDA
|
||
if half:
|
||
model.half()
|
||
|
||
# Set Dataloader
|
||
vid_path, vid_writer = None, None
|
||
if webcam:
|
||
view_img = True
|
||
torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
|
||
dataset = LoadStreams(source, img_size=img_size)
|
||
else:
|
||
save_img = True
|
||
dataset = LoadImages(source, img_size=img_size)
|
||
|
||
# Get names and colors
|
||
names = load_classes(opt.names)
|
||
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
|
||
|
||
# Run inference
|
||
t0 = time.time()
|
||
img = torch.zeros((1, 3, img_size, img_size), device=device) # init img
|
||
_ = model(img.half() if half else img.float()) if device.type != 'cpu' else None # run once
|
||
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)
|
||
|
||
# Inference
|
||
t1 = torch_utils.time_synchronized()
|
||
pred = model(img, augment=opt.augment)[0]
|
||
t2 = torch_utils.time_synchronized()
|
||
|
||
# to float
|
||
if half:
|
||
pred = pred.float()
|
||
|
||
# Apply NMS
|
||
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
|
||
multi_label=False, classes=opt.classes, agnostic=opt.agnostic_nms)
|
||
|
||
# Apply Classifier
|
||
if classify:
|
||
pred = apply_classifier(pred, modelc, img, im0s)
|
||
|
||
# Process detections
|
||
for i, det in enumerate(pred): # detections for image i
|
||
if webcam: # batch_size >= 1
|
||
p, s, im0 = path[i], '%g: ' % i, im0s[i]
|
||
else:
|
||
p, s, im0 = path, '', im0s
|
||
|
||
save_path = str(Path(out) / Path(p).name)
|
||
s += '%gx%g ' % img.shape[2:] # print string
|
||
gn = torch.tensor(im0s.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||
if det is not None and 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 += '%g %ss, ' % (n, names[int(c)]) # add to string
|
||
|
||
# Write results
|
||
for *xyxy, conf, cls in det:
|
||
if save_txt: # Write to file
|
||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||
with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
|
||
file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
|
||
|
||
if save_img or view_img: # Add bbox to image
|
||
label = '%s %.2f' % (names[int(cls)], conf)
|
||
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
|
||
|
||
# Print time (inference + NMS)
|
||
print('%sDone. (%.3fs)' % (s, t2 - t1))
|
||
|
||
# Stream results
|
||
if view_img:
|
||
cv2.imshow(p, im0)
|
||
if cv2.waitKey(1) == ord('q'): # q to quit
|
||
raise StopIteration
|
||
|
||
# Save results (image with detections)
|
||
if save_img:
|
||
if dataset.mode == 'images':
|
||
cv2.imwrite(save_path, im0)
|
||
else:
|
||
if vid_path != save_path: # new video
|
||
vid_path = save_path
|
||
if isinstance(vid_writer, cv2.VideoWriter):
|
||
vid_writer.release() # release previous video writer
|
||
|
||
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))
|
||
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
|
||
vid_writer.write(im0)
|
||
|
||
if save_txt or save_img:
|
||
print('Results saved to %s' % os.getcwd() + os.sep + out)
|
||
if platform == 'darwin': # MacOS
|
||
os.system('open ' + save_path)
|
||
|
||
print('Done. (%.3fs)' % (time.time() - t0))
|
||
|
||
|
||
if __name__ == '__main__':
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
|
||
parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path')
|
||
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
|
||
parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
|
||
parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
|
||
parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
|
||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||
parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
|
||
parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
|
||
parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
|
||
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
|
||
parser.add_argument('--view-img', action='store_true', help='display results')
|
||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||
parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
|
||
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||
opt = parser.parse_args()
|
||
print(opt)
|
||
|
||
with torch.no_grad():
|
||
detect()
|