def ImgDetect(img_path,
net,
meta,
darknet_data,
save_path='./',
noshow_img=True,
save_img=False):
import YoloObj
img = cv2.imread(img_path)
results = DFUNC.detect(net,
meta,
bytes(img_path, encoding='utf-8'),
thresh=float(args.thresh))
objs = []
for result in results:
obj = YoloObj.DetectedObj(result)
objs.append(obj)
for obj in objs:
print(obj.obj_string, obj.cx, obj.cy)
print('Number of objects: ', len(objs), '\n')
YoloObj.DrawBBox(objs,
img,
show=not noshow_img,
save=save_img,
save_path=save_path)
return objs
def ImgDetect(net, meta, img_np, thresh=0.25):
# net: DFUNC.load_net(bytes(darknet_cfg, 'utf-8'),
# bytes(darknet_weights, 'utf-8'), 0)
# meta: DFUNC.load_meta(bytes(darknet_data, 'utf-8'))
# img_np: image in numpy array
results = DFUNC.detect(net, meta, img_np, thresh)
objs = []
for result in results:
obj = YoloObj.DetectedObj(result)
objs.append(obj)
# sort the objects by confidence
objs = sorted(objs, key=lambda x: x.conf, reverse=True)
return objs