把DepthAI作为发生器使用
如果你想把DepthAI集成到你的项目中,这个例子可能会对你有用,因为它把 “如何 获得结果 “和 “用它们 做什么 “分开了。
这个例子使用yield
关键字将结果发送到for循环,而for循环调用了这个方法。
如果你想在你的自定义代码中进一步处理接收到的帧,就能用得上这个例子。
Code
import consts.resource_paths
import cv2
import depthai
class DepthAI:
def __init__(self):
if not depthai.init_device(consts.resource_paths.device_cmd_fpath):
raise RuntimeError("Error initializing device. Try to reset it.")
self.p = depthai.create_pipeline(config={
"streams": ["metaout", "previewout"],
"ai": {
"blob_file": "/path/to/model.blob",
"blob_file_config": "/path/to/config.json"
}
})
self.entries_prev = []
def run(self):
while True:
nnet_packets, data_packets = self.p.get_available_nnet_and_data_packets()
for _, nnet_packet in enumerate(nnet_packets):
self.entries_prev = []
for _, e in enumerate(nnet_packet.entries()):
if e[0]['image_id'] == -1.0 or e[0]['conf'] == 0.0:
break
if e[0]['conf'] > 0.5:
self.entries_prev.append(e[0])
for packet in data_packets:
if packet.stream_name == 'previewout':
data = packet.getData()
data0 = data[0, :, :]
data1 = data[1, :, :]
data2 = data[2, :, :]
frame = cv2.merge([data0, data1, data2])
img_h = frame.shape[0]
img_w = frame.shape[1]
results = []
for e in self.entries_prev:
pt1 = int(e['x_min'] * img_w), int(e['y_min'] * img_h)
pt2 = int(e['x_max'] * img_w), int(e['y_max'] * img_h)
results.append((pt1, pt2))
yield frame, results
def __del__(self):
del self.p
depthai.deinit_device()
d = DepthAI()
for frame, results in d.run():
for pt1, pt2 in results:
cv2.rectangle(frame, pt1, pt2, (0, 0, 255), 2)
cv2.imshow('previewout', frame)
if cv2.waitKey(1) == ord('q'):
break
del d