cv2.addWeighted(seg_image, config.ALPHA, frame,
1 - config.ALPHA, 0, frame)
vis_text(frame, "fps: {}".format(fps.fps_local()),
(10, 30))
# boxes (ymin, xmin, ymax, xmax)
if config.BBOX:
map_labeled = measure.label(seg_map, connectivity=1)
for region in measure.regionprops(map_labeled):
if region.area > config.MINAREA:
box = region.bbox
p1 = (box[1], box[0])
p2 = (box[3], box[2])
cv2.rectangle(frame, p1, p2, (77, 255, 9), 2)
vis_text(
frame, config.LABEL_NAMES[seg_map[tuple(
region.coords[0])]],
(p1[0], p1[1] - 10))
cv2.imshow(config.DL_MODEL_NAME, frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
fps.update()
fps.stop()
vs.stop()
if __name__ == '__main__':
config = Config()
model = Model('dl', config.DL_MODEL_NAME,
config.DL_MODEL_PATH).prepare_dl_model()
segmentation(model, config)
models = np.squeeze(models)
models.sort()
print("> start testing following sequention of models: \n{}".format(models))
for mod in models:
print("> testing model: {}".format(mod))
# conditionals
optimized=False
single_class=False
# Test Model
if 'hands' in mod or 'person' in mod:
single_class=True
if 'deeplab' in mod:
config = create_test_config(mod,'DL',optimized,single_class)
print("TEST")
model = Model('dl', config.DL_MODEL_NAME, config.DL_MODEL_PATH).prepare_dl_model()
segmentation(model,config)
else:
config = create_test_config(mod,'OD',optimized,single_class)
model = Model('od', config.OD_MODEL_NAME, config.OD_MODEL_PATH, config.LABEL_PATH,
config.NUM_CLASSES, config.SPLIT_MODEL, config.SSD_SHAPE).prepare_od_model()
detection(model,config)
# Check if there is an optimized graph
model_dir = os.path.join(os.getcwd(),'models',mod)
for root, dirs, files in os.walk(model_dir):
for file in files:
if 'optimized' in file:
optimized=True
print '> found: optimized graph'
def main():
config = Config()
model = Model('od', config.OD_MODEL_NAME, config.OD_MODEL_PATH,
config.LABEL_PATH, config.NUM_CLASSES, config.SPLIT_MODEL,
config.SSD_SHAPE).prepare_od_model()
detection(model, config)
