175 lines
7.0 KiB
Python
175 lines
7.0 KiB
Python
import argparse
|
|
from sys import platform
|
|
|
|
from models import * # set ONNX_EXPORT in models.py
|
|
from utils.datasets import *
|
|
from utils.utils import *
|
|
|
|
|
|
def detect(save_txt=False, 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 = opt.output, opt.source, opt.weights, opt.half, opt.view_img
|
|
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()
|
|
|
|
# Fuse Conv2d + BatchNorm2d layers
|
|
# model.fuse()
|
|
|
|
# Eval mode
|
|
model.to(device).eval()
|
|
|
|
# Export mode
|
|
if ONNX_EXPORT:
|
|
img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192)
|
|
torch.onnx.export(model, img, 'weights/export.onnx', verbose=False, opset_version=10)
|
|
|
|
# Validate exported model
|
|
import onnx
|
|
model = onnx.load('weights/export.onnx') # 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, half=half)
|
|
else:
|
|
save_img = True
|
|
dataset = LoadImages(source, img_size=img_size, half=half)
|
|
|
|
# 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()
|
|
for path, img, im0s, vid_cap in dataset:
|
|
t = time.time()
|
|
|
|
# Get detections
|
|
img = torch.from_numpy(img).to(device)
|
|
if img.ndimension() == 3:
|
|
img = img.unsqueeze(0)
|
|
pred = model(img)[0]
|
|
|
|
if opt.half:
|
|
pred = pred.float()
|
|
|
|
# Apply NMS
|
|
pred = non_max_suppression(pred, opt.conf_thres, opt.nms_thres)
|
|
|
|
# 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 = 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
|
|
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
|
|
|
|
# Print time (inference + NMS)
|
|
print('%sDone. (%.3fs)' % (s, time.time() - t))
|
|
|
|
# Write results
|
|
for *xyxy, conf, cls in det:
|
|
if save_txt: # Write to file
|
|
with open(save_path + '.txt', 'a') as file:
|
|
file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
|
|
|
|
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)])
|
|
|
|
# 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 ' + out + ' ' + 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.weights', help='path to weights file')
|
|
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=416, help='inference size (pixels)')
|
|
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
|
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
|
|
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')
|
|
opt = parser.parse_args()
|
|
print(opt)
|
|
|
|
with torch.no_grad():
|
|
detect()
|