Digital Zoom

1#!/usr/bin/env python3
2
3import cv2
4import depthai as dai
5import time
6
7# This example shows how to crop out the biggest person from the maximum resolution of the camera and setting the autofocus and autoexposure region to the same ROI.
8
9
10def getBiggestPerson(imgDetections: dai.ImgDetections):
11    biggestDetection = None
12    biggestDetectionSize = 0
13    for detection in imgDetections.detections:
14        if detection.label == 0: # Person
15            size = (detection.xmax - detection.xmin) * (detection.ymax - detection.ymin)
16            if size > biggestDetectionSize:
17                biggestDetection = detection
18                biggestDetectionSize = size
19    return biggestDetection
20
21
22def displayFrame(name: str, frame: dai.ImgFrame, imgDetections: dai.ImgDetections, labelMap: dict):
23    color = (0, 255, 0)
24    assert imgDetections.getTransformation() is not None
25    cvFrame = frame.getFrame() if frame.getType() == dai.ImgFrame.Type.RAW16 else frame.getCvFrame()
26    for detection in imgDetections.detections:
27        # Get the shape of the frame from which the detections originated for denormalization
28        normShape = imgDetections.getTransformation().getSize()
29
30        # Create rotated rectangle to remap
31        rotRect = dai.RotatedRect(dai.Rect(dai.Point2f(detection.xmin, detection.ymin), dai.Point2f(detection.xmax, detection.ymax)).denormalize(normShape[0], normShape[1]), 0)
32        # Remap the detection rectangle to target frame
33        remapped = imgDetections.getTransformation().remapRectTo(frame.getTransformation(), rotRect)
34        # Remapped rectangle could be rotated, so we get the bounding box
35        bbox = [int(l) for l in remapped.getOuterRect()]
36        cv2.putText(
37            cvFrame,
38            labelMap[detection.label],
39            (bbox[0] + 10, bbox[1] + 20),
40            cv2.FONT_HERSHEY_TRIPLEX,
41            0.5,
42            255,
43        )
44        cv2.putText(
45            cvFrame,
46            f"{int(detection.confidence * 100)}%",
47            (bbox[0] + 10, bbox[1] + 40),
48            cv2.FONT_HERSHEY_TRIPLEX,
49            0.5,
50            255,
51        )
52        cv2.rectangle(cvFrame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
53    # Show the frame
54    cv2.imshow(name, cvFrame)
55
56
57def transformDetectionToSource(imgDetections: dai.ImgDetections, detection: dai.ImgDetection):
58    normShape = imgDetections.getTransformation().getSize()
59    sourceSize = imgDetections.getTransformation().getSourceSize()
60    width, height = sourceSize
61    rotRect = dai.RotatedRect(dai.Rect(dai.Point2f(detection.xmin, detection.ymin), dai.Point2f(detection.xmax, detection.ymax)).denormalize(normShape[0], normShape[1]), 0)
62    rotRect = imgDetections.getTransformation().invTransformRect(rotRect)
63    outerRect = rotRect.getOuterRect()
64
65    firstPoint = dai.Point2f(max(0, min(outerRect[0], width)), max(0, min(outerRect[1], height)))
66    secondPoint = dai.Point2f(max(0, min(outerRect[2], width)), max(0, min(outerRect[3], height)))
67    return dai.Rect(firstPoint, secondPoint)
68
69
70device = dai.Device()
71modelPath = dai.getModelFromZoo(dai.NNModelDescription("yolov6-nano", platform=device.getPlatformAsString()))
72modelArchive = dai.NNArchive(modelPath)
73inputSize = modelArchive.getInputSize()
74type = modelArchive.getConfig().model.inputs[0].preprocessing.daiType
75
76if type:
77    try:
78        frameType = dai.ImgFrame.Type.__getattribute__(type)
79    except AttributeError:
80        type = None
81
82if not type:
83    if device.getPlatform() == dai.Platform.RVC2:
84        frameType = dai.ImgFrame.Type.BGR888p
85    else:
86        frameType = dai.ImgFrame.Type.BGR888i
87
88# Create pipeline
89with dai.Pipeline(device) as pipeline:
90    # Define source and output
91    cam = pipeline.create(dai.node.Camera).build()
92    cameraControlQueue = cam.inputControl.createInputQueue()
93    fullResStream = cam.requestFullResolutionOutput(useHighestResolution=True)
94
95    imageManip = pipeline.create(dai.node.ImageManip)
96    imageManip.initialConfig.setOutputSize(inputSize[0], inputSize[1])
97    imageManip.initialConfig.setFrameType(frameType)
98
99    fullResStream.link(imageManip.inputImage)
100
101    detectionNetwork = pipeline.create(dai.node.DetectionNetwork).build(imageManip.out, modelArchive)
102    labelMap = detectionNetwork.getClasses()
103
104    imageManipCropOut = pipeline.create(dai.node.ImageManip)
105    imageManipCropOut.setMaxOutputFrameSize(round(1000*1000*1.5)+300)
106    imageManipCropOut.initialConfig.setOutputSize(800, 800)
107    imageManipCropOut.inputImage.setBlocking(False)
108    imageManipCropOut.inputImage.setMaxSize(1)
109
110    imageManipConfigQueue = imageManipCropOut.inputConfig.createInputQueue()
111    imageManipCropOutQueue = imageManipCropOut.out.createOutputQueue()
112    fullResStream.link(imageManipCropOut.inputImage)
113
114    videoQueue = detectionNetwork.passthrough.createOutputQueue()
115    detectionQueue = detectionNetwork.out.createOutputQueue()
116
117    # Connect to device and start pipeline
118    pipeline.start()
119    lastTimeToAutoFocus = time.time()
120    while pipeline.isRunning():
121        videoIn = videoQueue.get()
122        detections = detectionQueue.get()
123        biggestDetection = getBiggestPerson(detections)
124        if biggestDetection:
125            sourceRect = transformDetectionToSource(detections, biggestDetection)
126            configQueue = dai.ImageManipConfig()
127            configQueue.addCrop(sourceRect, False)
128            configQueue.setOutputSize(800, 800, dai.ImageManipConfig.ResizeMode.LETTERBOX)
129            imageManipConfigQueue.send(configQueue)
130
131            configCamera = dai.CameraControl()
132            configCamera.setAutoExposureRegion(int(sourceRect.x), int(sourceRect.y), int(sourceRect.width), int(sourceRect.height))
133            if(time.time() - lastTimeToAutoFocus > 5):
134                lastTimeToAutoFocus = time.time()
135                configCamera.setAutoFocusRegion(int(sourceRect.x), int(sourceRect.y), int(sourceRect.width), int(sourceRect.height))
136            cameraControlQueue.send(configCamera)
137        imageManipCropOutFrame = imageManipCropOutQueue.tryGet()
138        if imageManipCropOutFrame is not None:
139            assert isinstance(imageManipCropOutFrame, dai.ImgFrame)
140            cv2.imshow("Cropped out frame", imageManipCropOutFrame.getCvFrame())
141        assert isinstance(videoIn, dai.ImgFrame)
142        assert isinstance(detections, dai.ImgDetections)
143        displayFrame("Full view video", videoIn, detections, labelMap)
144        key = cv2.waitKey(1)
145        if key == ord("q"):
146            break