Here is the full code of my projects (big thanks to KonstantinosAng for mapper functions and guanming001 for the basic_2d examples)

from asyncore import read from pykinect2.PyKinectV2 import * from pykinect2 import PyKinectV2 from pykinect2 import PyKinectRuntime import numpy as np import cv2 import mapper import time

net = cv2.dnn.readNet("yolov3-tiny_training_last.weights", "yolov3-tiny_testing (1).cfg") net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)

classes = [] with open("obj.names", "r") as f: classes = [line.strip() for line in f.readlines()] layer_names = net.getLayerNames() output_layers = [layer_names[i-1] for i in net.getUnconnectedOutLayers()] colors = np.random.uniform(0, 255, size=(len(classes), 3)) #Generate Random Color font = cv2.FONT_HERSHEY_SIMPLEX timeframe = time.time() frame_id = 0 maskx=0 masky=0 no_maskx=0 no_masky=0

kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Color | PyKinectV2.FrameSourceTypes_Depth) depth_width, depth_height = kinect.depth_frame_desc.Width, kinect.depth_frame_desc.Height # Default: 512, 424 color_width, color_height = kinect.color_frame_desc.Width, kinect.color_frame_desc.Height # Default: 1920, 1080

while True:

##############################
### Get images from kinect ###
##############################
if kinect.has_new_color_frame() and \
   kinect.has_new_depth_frame() :

    depth_frame = kinect.get_last_depth_frame()
    color_frame = kinect.get_last_color_frame()

    depth_img = depth_frame.reshape(((depth_height, depth_width))).astype(np.uint16)
    color_img = color_frame.reshape(((color_height, color_width, 4))).astype(np.uint8)

    color_img_resize = cv2.resize(color_img, (0,0), fx=0.5, fy=0.5) # Resize (1080, 1920, 4) into half (540, 960, 4)
    depth_colormap   = cv2.applyColorMap(cv2.convertScaleAbs(depth_img, alpha=255/1500), cv2.COLORMAP_JET) # Scale to display from 0 mm to 1500 mm

    frame = color_img_resize
    depth = depth_colormap
    frame_id += 1
    color_height, color_width, channels = frame.shape

    #Detecting Object
    blob = cv2.dnn.blobFromImage(frame, 1/255, (320, 320), (0, 0, 0), True, crop=False)

    net.setInput(blob)
    outs = net.forward(output_layers)

    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0: #Confidence Level -> Accuracy
                # Object detected
                center_x = int(detection[0] * color_width)
                center_y = int(detection[1] * color_height)
                w = int(detection[2] * color_width)
                h = int(detection[3] * color_height)

                # Rectangle coordinates
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)

                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.2, 0.4)

    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            confidence = confidences[i]
            # color = colors[i]
            color = (255,255,255)
            if label == "mask" :
                no_maskx = 0
                no_masky = 0
                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
                cv2.putText(frame, label, (x, y+30), font, 1, color, 2)
                cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y+30), font, 1, color, 2)
                center = ((x+w/2)-(color_width/2), (y+h/2)-(color_height/2))
                maskx = (x+w/2)
                masky = (y+h/2)
                print (maskx,masky)
            if label == "no_mask":
                maskx = 0
                masky = 0
                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
                cv2.putText(frame, label, (x, y+30), font, 1, color, 2)
                cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y+30), font, 1, color, 2)
                center = ((x+w/2)-(color_width/2), (y+h/2)-(color_height/2))
                no_maskx = (x+w/2)
                no_masky = (y+h/2)
                #print (no_maskx,no_masky)
                mapped_no_maskx = no_maskx/0.4
                mapped_no_masky = no_masky/0.4
                depth_x, depth_y = mapper.color_point_2_depth_point(kinect, _DepthSpacePoint, kinect._depth_frame_data, [mapped_no_maskx, mapped_no_masky]) # pixel        
    elapsed_time = time.time() - timeframe
    fps = frame_id / elapsed_time
    cv2.putText(frame, str(round(fps,2)), (10, 50), font, 2, (255, 255, 255), 2) #FPS Value
    cv2.putText(frame, "FPS", (220, 50), font, 2, (255, 255, 255), 2) #FPS Label
    cv2.imshow("Image", frame)
    cv2.circle(color_img_resize, (960,540), radius=10, color=(0, 0, 0), thickness=2)
    cv2.circle(depth_colormap, (960,540), radius=10, color=(0, 0, 0), thickness=2)
    cv2.putText(depth, str(round(fps,2)), (10, 50), font, 2, (255, 255, 255), 2) #FPS Value
    cv2.putText(depth, "FPS", (220, 50), font, 2, (255, 255, 255), 2) #FPS Label
    cv2.imshow("dotted Object", depth)
    cv2.imshow('color', frame)
    cv2.imshow('depth', depth)
    depth_x, depth_y = mapper.color_point_2_depth_point(kinect, _DepthSpacePoint, kinect._depth_frame_data, [960,540]) # pixel
    if (int(depth_y ) * 512 + int(depth_x)) < 512 * 424:
        depth_z = float(depth_frame[int(depth_y ) * 512 + int(depth_y )]) # mm
    else:
        # If it exceeds return the last value to catch overflow
        depth_z = float(depth_frame[int((512 * 424) - 1)]) # mm
    print (depth_z)
key = cv2.waitKey(1)
if key == 27:
    break

kinect.close() cv2.destroyAllWindows

here's the error for the code, i have done YOLOv3 with a webcam before and the data types seems to be the same as the variable color_img_resize, kinda confused here bcs apparently color_img_resize doesnt have the same input channels as the one with the webcam as an input

[ERROR:0@2.014] global D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\net_impl.cpp (1171) cv::dnn::dnn4_v20220524::Net::Impl::getLayerShapesRecursively OPENCV/DNN: [Convolution]:(conv_0): getMemoryShapes() throws exception. inputs=1 outputs=0/1 blobs=1
[ERROR:0@2.032] global D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\net_impl.cpp (1174) cv::dnn::dnn4_v20220524::Net::Impl::getLayerShapesRecursively input[0] = [ 1 4 320 320 ] [ERROR:0@2.054] global D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\net_impl.cpp (1182) cv::dnn::dnn4_v20220524::Net::Impl::getLayerShapesRecursively blobs[0] = CV_32FC1 [ 16 3 3 3 ]
[ERROR:0@2.069] global D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\net_impl.cpp (1184) cv::dnn::dnn4_v20220524::Net::Impl::getLayerShapesRecursively Exception message: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\layers\convolution_layer.cpp:405: error: (-2:Unspecified error) Number of input Traceback (most recent call last): File "cv2--kinect.py", line 62, in outs = net.forward(output_layers) cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\layers\convolution_layer.cpp:405: error: (-2:Unspecified error) Number of input channels should be multiple of 3 but got 4 in function 'cv::dnn::ConvolutionLayerImpl::getMemoryShapes'

for anyone wondering here's the code for the working YOLOv3 object detection with webcam : import time import numpy as np import cv2 import time

Load Yolo

net = cv2.dnn.readNet("weight/yolov3-tiny.weights", "cfg/yolov3-tiny.cfg")

net = cv2.dnn.readNet("yolov3-tiny_training_last.weights", "yolov3-tiny_testing (1).cfg") net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)

Load Video

cap = cv2.VideoCapture(0) #0 -> Webcam; "/path"

cap = cv2.imread("masker01132.jpg")

font = cv2.FONT_HERSHEY_SIMPLEX

timeframe = time.time() frame_id = 0 maskx=0 masky=0 no_maskx=0 no_masky=0

while True: , frame = cap.read() , dots = cap.read() frame_id += 1 height, width, channels = frame.shape

# Detecting objects
blob = cv2.dnn.blobFromImage(frame, 1/255, (320, 320), (0, 0, 0), True, crop=False)

net.setInput(blob)
outs = net.forward(output_layers)

# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0: #Confidence Level -> Accuracy
            # Object detected
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # Rectangle coordinates
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)

            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.2, 0.4)

for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        confidence = confidences[i]
        # color = colors[i]
        color = (255,255,255)
        if label == "mask" :
            no_maskx = 0
            no_masky = 0
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y+30), font, 1, color, 2)
            cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y+30), font, 1, color, 2)
            center = ((x+w/2)-(width/2), (y+h/2)-(height/2))
            maskx = (x+w/2)
            masky = (y+h/2)
            print (maskx,masky)
        if label == "no_mask":
            maskx = 0
            masky = 0
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y+30), font, 1, color, 2)
            cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y+30), font, 1, color, 2)
            center = ((x+w/2)-(width/2), (y+h/2)-(height/2))
            no_maskx = (x+w/2)
            no_masky = (y+h/2)
            print (no_maskx,no_masky)

elapsed_time = time.time() - timeframe
fps = frame_id / elapsed_time
cv2.putText(frame, str(round(fps,2)), (10, 50), font, 2, (255, 255, 255), 2) #FPS Value
cv2.putText(frame, "FPS", (220, 50), font, 2, (255, 255, 255), 2) #FPS Label
#frame_resize = cv2.resize(frame, (720,480), interpolation= cv2.INTER_LINEAR)
cv2.imshow("Image", frame)
cv2.circle(dots, (int(maskx),int(masky)), radius=2, color=(0, 0, 255), thickness=2)
cv2.circle(dots, (int(no_maskx),int(no_masky)), radius=2, color=(0, 0, 255), thickness=2)
cv2.putText(dots, str(round(fps,2)), (10, 50), font, 2, (255, 255, 255), 2) #FPS Value
cv2.putText(dots, "FPS", (220, 50), font, 2, (255, 255, 255), 2) #FPS Label
#dots_resize = cv2.resize(dots, (720,480), interpolation= cv2.INTER_LINEAR)
cv2.imshow("dotted Object", dots)
key = cv2.waitKey(1)
if key == 27: #Escape
    break

cap.release() cv2.destroyAllWindows()

Kinect / PyKinect2

PyKinect2 Integration wih OpenCV for YOLOv3 throws an error of : Number of input channels should be multiple of 3 but got 4 in function #104

Load Yolo

net = cv2.dnn.readNet("weight/yolov3-tiny.weights", "cfg/yolov3-tiny.cfg")

Load Video

cap = cv2.imread("masker01132.jpg")