Hi pierreia .
The only problem in the code above is that it's not synced. You could either sync frames+detections with host-side syncing, or just use passthrough frame like I did below:
#!/usr/bin/env python3
from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
from time import monotonic
import blobconverter
# Get argument first
nnPath = 'mobilenet-ssd_openvino_2021.4_8shave.blob'
videoPath = 'traffic_5mn.mp4'
if len(sys.argv) > 2:
nnPath = sys.argv[1]
videoPath = sys.argv[2]
if not Path(nnPath).exists() or not Path(videoPath).exists():
import sys
raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
# MobilenetSSD label texts
labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
"diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
# Create pipeline
pipeline = dai.Pipeline()
# Define sources and outputs
nn = pipeline.create(dai.node.MobileNetDetectionNetwork)
xinFrame = pipeline.create(dai.node.XLinkIn)
xinFrame.setStreamName("inFrame")
xinFrame.out.link(nn.input)
# Properties
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(True)
# Linking
nnOut = pipeline.create(dai.node.XLinkOut)
nnOut.setStreamName("nn")
nn.out.link(nnOut.input)
nnPass = pipeline.create(dai.node.XLinkOut)
nnPass.setStreamName("pass")
nn.passthrough.link(nnPass.input)
# Connect to device and start pipeline
with dai.Device(pipeline) as device:
# Input queue will be used to send video frames to the device.
qIn = device.getInputQueue(name="inFrame")
# Output queue will be used to get nn data from the video frames.
qDet = device.getOutputQueue(name="nn", maxSize=6, blocking=True)
qPass = device.getOutputQueue("pass")
frame = None
detections = []
# nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
def frameNorm(frame, bbox):
normVals = np.full(len(bbox), frame.shape[0])
normVals[::2] = frame.shape[1]
return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
return cv2.resize(arr, shape).transpose(2, 0, 1).flatten()
def displayFrame(name, frame):
for detection in detections:
bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
# Show the frame
cv2.imshow(name, frame)
cap = cv2.VideoCapture(videoPath)
while cap.isOpened():
read_correctly, frame = cap.read()
if not read_correctly:
break
img = dai.ImgFrame()
resized = to_planar(frame, (300, 300))
img.setTimestamp(monotonic())
img.setType(dai.RawImgFrame.Type.BGR888p)
img.setSize(300, 300)
img.setData(resized)
qIn.send(img)
inDet = qDet.tryGet()
if inDet is not None:
detections = inDet.detections
frame = qPass.get().getCvFrame()
displayFrame("passthrough", frame)
if cv2.waitKey(1) == ord('q'):
break
