depthai-python/examples/MobileNet/rgb_mobilenet.py at main · moratom/depthai-python · GitHub

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#!/usr/bin/env python3

from pathlib import Path

import cv2

import depthai as dai

import numpy as np

import time

import argparse

nnPathDefault = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())

parser = argparse.ArgumentParser()

parser.add_argument('nnPath', nargs='?', help="Path to mobilenet detection network blob", default=nnPathDefault)

parser.add_argument('-s', '--sync', action="store_true", help="Sync RGB output with NN output", default=False)

args = parser.parse_args()

if not Path(nnPathDefault).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

camRgb = pipeline.create(dai.node.ColorCamera)

nn = pipeline.create(dai.node.MobileNetDetectionNetwork)

xoutRgb = pipeline.create(dai.node.XLinkOut)

nnOut = pipeline.create(dai.node.XLinkOut)

nnNetworkOut = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("rgb")

nnOut.setStreamName("nn")

nnNetworkOut.setStreamName("nnNetwork");

# Properties

camRgb.setPreviewSize(300, 300)

camRgb.setInterleaved(False)

camRgb.setFps(40)

# Define a neural network that will make predictions based on the source frames

nn.setConfidenceThreshold(0.5)

nn.setBlobPath(args.nnPath)

nn.setNumInferenceThreads(2)

nn.input.setBlocking(False)

# Linking

if args.sync:

nn.passthrough.link(xoutRgb.input)

else:

camRgb.preview.link(xoutRgb.input)

camRgb.preview.link(nn.input)

nn.out.link(nnOut.input)

nn.outNetwork.link(nnNetworkOut.input);

# Connect to device and start pipeline

with dai.Device(pipeline) as device:

# Output queues will be used to get the rgb frames and nn data from the outputs defined above

qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)

qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)

qNN = device.getOutputQueue(name="nnNetwork", maxSize=4, blocking=False);

frame = None

detections = []

startTime = time.monotonic()

counter = 0

color2 = (255, 255, 255)

# nn data (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 displayFrame(name, frame):

color = (255, 0, 0)

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, color)

cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)

cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)

# Show the frame

cv2.imshow(name, frame)

printOutputLayersOnce = True

while True:

if args.sync:

# Use blocking get() call to catch frame and inference result synced

inRgb = qRgb.get()

inDet = qDet.get()

inNN = qNN.get()

else:

# Instead of get (blocking), we use tryGet (non-blocking) which will return the available data or None otherwise

inRgb = qRgb.tryGet()

inDet = qDet.tryGet()

inNN = qNN.tryGet()

if inRgb is not None:

frame = inRgb.getCvFrame()

cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),

(2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)

if inDet is not None:

detections = inDet.detections

counter += 1

if printOutputLayersOnce and inNN is not None:

toPrint = 'Output layer names:'

for ten in inNN.getAllLayerNames():

toPrint = f'{toPrint} {ten},'

print(toPrint)

printOutputLayersOnce = False;

# If the frame is available, draw bounding boxes on it and show the frame

if frame is not None:

displayFrame("rgb", frame)

if cv2.waitKey(1) == ord('q'):

break

depthai-python/examples/MobileNet/rgb_mobilenet.py at main · moratom/depthai-python · GitHub

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