Using DepthAI with generators
If you want to integrate the DepthAI into your project, this example might be useful for you as it splits the “how to get the results”, from “what to do with them”.
This example uses yield
keyword to send the results to for loop, which called the method.
It’s useful if you want to process the received frames further in your custom code
Code
import cv2
import depthai
class DepthAI:
def __init__(self):
self.device = depthai.Device('', False)
self.p = self.device.create_pipeline(config={
"streams": ["metaout", "previewout"],
"ai": {
"blob_file": "/path/to/model.blob",
"blob_file_config": "/path/to/config.json"
}
})
self.detections = []
def run(self):
while True:
nnet_packets, data_packets = self.p.get_available_nnet_and_data_packets()
for nnet_packet in nnet_packets:
self.detections = list(nnet_packet.getDetectedObjects())
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 detection in self.detections:
pt1 = int(detection.x_min * img_w), int(detection.y_min * img_h)
pt2 = int(detection.x_max * img_w), int(detection.y_max * img_h)
results.append((pt1, pt2))
yield frame, results
def __del__(self):
del self.p
del self.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