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HD USB Camera📖
01.Basic course
  • 1.Camera parameters
  • 2.Camera detect
  • 3.Camera preview screen
02.Docker course
  • 1.Docker overview and docker installation
  • 2.Common commands for docker image containers
  • 3.Docker images deeply understand and publish images
  • 4.Docker hardware interaction and data processing
  • 5.Enter the bot's docker container
  • 6.Robot development environment construction in Docker
  • 7.How customers can update docker images in the future
03.ROS vision course
  • 0.Camera preview screen
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.ROS+Opencv basics
  • 6.Face recognition
  • 7.harris corner detection
  • 8.Target tracking algorithm
  • 9.Contour moment
  • 10.Polygon outline
  • 11.Discrete fourier transform algorithm
  • 12.Edge detection algorithm
  • 13.Face detection algorithm
  • 14.Optical flow detection algorithm
  • 15.Contour detection
  • 16.General contour detection
  • 17.Feature point tracking
  • 18.HLS color filtering
  • 19.Hough circle detection
  • 20.Hough linear detection
  • 21.HSV color filtering
  • 22.LK optical flow algorithm
  • 23.Detection algorithm
  • 24.Phase dependent displacement detection
  • 25.Image pyramid sampling algorithm
  • 26.RGB color filtering
  • 27.Clear background detection algorithm
  • 28.Simplified optical flow algorithm
  • 29.Simple filter
  • 30.Threshold image processing
  • 31.Watershed segmentation algorithm
04.Mediapipe course
  • 1.Hand detection
  • 2.Attitude detection
  • 3.Overall inspection
  • 4.Face detection
  • 5.Face recognition
  • 6.Face effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Gesture recognition
05.Robot Vision course
  • Tracking color line
06.Camera preview and frame rate test
  • Camera preview and frame rate test
Download
  • PC_Test_Software
  • Docker_Image
Download ZIP

Welcome to HD USB Camera repository

8.Brush

Brush

1. Introduction

MediaPipe is an open source data stream processing machine learning application development framework developed by Google. It is a graph-based data processing pipeline for building and using multiple forms of data sources, such as video, audio, sensor data, and any time series data. MediaPipe is cross-platform and can run on embedded platforms (Raspberry Pi, etc.), mobile devices (iOS and Android), workstations and servers, and supports mobile GPU acceleration. MediaPipe provides cross-platform, customizable ML solutions for real-time and streaming media.

The core framework of MediaPipe is implemented in C++ and provides support for languages such as Java and Objective C. The main concepts of MediaPipe include Packet, Stream, Calculator, Graph and Subgraph.

Features of MediaPipe:

  • End-to-end acceleration: Built-in fast ML inference and processing accelerates even on commodity hardware.
  • Build once, deploy anywhere: Unified solution for Android, iOS, desktop/cloud, web and IoT.
  • Ready-to-use solutions: cutting-edge ML solutions that showcase the full capabilities of the framework.
  • Free and open source: frameworks and solutions under Apache2.0, fully extensible and customizable.

2. Brush

When the right middle finger and middle finger are combined, they are in the selection state, and a color selection box pops up at the same time. When the two fingertips move to the corresponding color position, the color is selected (black is the eraser);When the thumb and middle finger are separated, the drawing state begins and you can draw anywhere on the drawing board.

2.1 Start up

Input command:

cd ~/yahboomcar_ws/src/yahboomcar_mediapipe/scripts
python3 09_VirtualPaint.py

image-20231016112807709

2.2 Source code

Code path:~/yahboomcar_ws/src/yahboomcar_mediapipe/scripts

​x
#!/usr/bin/env python3
# encoding: utf-8
import math
import time
import cv2 as cv
import numpy as np
import mediapipe as mp

xp = yp = pTime = boxx = 0
tipIds = [4, 8, 12, 16, 20]
imgCanvas = np.zeros((480, 640, 3), np.uint8)
brushThickness = 5
eraserThickness = 100
top_height = 50
Color = "Red"
ColorList = {
    'Red': (0, 0, 255),
    'Green': (0, 255, 0),
    'Blue': (255, 0, 0),
    'Yellow': (0, 255, 255),
    'Black': (0, 0, 0),
}

class handDetector:
    def __init__(self, mode=False, maxHands=2, detectorCon=0.5, trackCon=0.5):
        self.tipIds = [4, 8, 12, 16, 20]
        self.mpHand = mp.solutions.hands
        self.mpDraw = mp.solutions.drawing_utils
        self.hands = self.mpHand.Hands(
            static_image_mode=mode,
            max_num_hands=maxHands,
            min_detection_confidence=detectorCon,
            min_tracking_confidence=trackCon )
        self.lmDrawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 0, 255), thickness=-1, circle_radius=15)
        self.drawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 255, 0), thickness=10, circle_radius=10)

    def findHands(self, frame, draw=True):
        self.lmList = []
        img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
        self.results = self.hands.process(img_RGB)
        if self.results.multi_hand_landmarks:
            for handLms in self.results.multi_hand_landmarks:
                if draw: self.mpDraw.draw_landmarks(frame, handLms, self.mpHand.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
                else: self.mpDraw.draw_landmarks(frame, handLms, self.mpHand.HAND_CONNECTIONS)
            for id, lm in enumerate(self.results.multi_hand_landmarks[0].landmark):
                h, w, c = frame.shape
                cx, cy = int(lm.x * w), int(lm.y * h)
                # print(id, cx, cy)
                self.lmList.append([id, cx, cy])
        return frame, self.lmList

    def fingersUp(self):
        fingers=[]
        # Thumb
        if (self.calc_angle(self.tipIds[0],
                            self.tipIds[0] - 1,
                            self.tipIds[0] - 2) > 150.0) and (
                self.calc_angle(
                    self.tipIds[0] - 1,
                    self.tipIds[0] - 2,
                    self.tipIds[0] - 3) > 150.0): fingers.append(1)
        else:
            fingers.append(0)
        # 4 finger
        for id in range(1, 5):
            if self.lmList[self.tipIds[id]][2] < self.lmList[self.tipIds[id] - 2][2]:
                fingers.append(1)
            else:
                fingers.append(0)
        return fingers



    def get_dist(self, point1, point2):
        x1, y1 = point1
        x2, y2 = point2
        return abs(math.sqrt(math.pow(abs(y1 - y2), 2) + math.pow(abs(x1 - x2), 2)))

    def calc_angle(self, pt1, pt2, pt3):
        point1 = self.lmList[pt1][1], self.lmList[pt1][2]
        point2 = self.lmList[pt2][1], self.lmList[pt2][2]
        point3 = self.lmList[pt3][1], self.lmList[pt3][2]
        a = self.get_dist(point1, point2)
        b = self.get_dist(point2, point3)
        c = self.get_dist(point1, point3)
        try:
            radian = math.acos((math.pow(a, 2) + math.pow(b, 2) - math.pow(c, 2)) / (2 * a * b))
            angle = radian / math.pi * 180
        except:
            angle = 0
        return abs(angle)



if __name__ == '__main__':
    capture = cv.VideoCapture(0)
    capture.set(6, cv.VideoWriter.fourcc('M', 'J', 'P', 'G'))
    capture.set(cv.CAP_PROP_FRAME_WIDTH, 640)
    capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480)
    print("capture get FPS : ", capture.get(cv.CAP_PROP_FPS))
    hand_detector = handDetector(detectorCon=0.85)
    while capture.isOpened():
        ret, frame = capture.read()
        # frame = cv.flip(frame, 1)
        h, w, c = frame.shape
        frame,lmList = hand_detector.findHands(frame, draw=False)
        if len(lmList) != 0:
            # print(lmList)
            # tip of index and middle fingers
            x1, y1 = lmList[8][1:]
            x2, y2 = lmList[12][1:]
            fingers = hand_detector.fingersUp()
            if fingers[1] and fingers[2]:
                # print("Seclection mode")
                if y1 < top_height:
                    if 0 < x1 < int(w / 5) - 1:
                        boxx = 0
                        Color = "Red"
                    if int(w / 5) < x1 < int(w * 2 / 5) - 1:
                        boxx = int(w / 5)
                        Color = "Green"
                    elif int(w * 2 / 5) < x1 < int(w * 3 / 5) - 1:
                        boxx = int(w * 2 / 5)
                        Color = "Blue"
                    elif int(w * 3 / 5) < x1 < int(w * 4 / 5) - 1:
                        boxx = int(w * 3 / 5)
                        Color = "Yellow"
                    elif int(w * 4 / 5) < x1 < w - 1:
                        boxx = int(w * 4 / 5)
                        Color = "Black"
                cv.rectangle(frame, (x1, y1 - 25), (x2, y2 + 25), ColorList[Color], cv.FILLED)
                cv.rectangle(frame, (boxx, 0), (boxx + int(w / 5), top_height), ColorList[Color], cv.FILLED)
                cv.rectangle(frame, (0, 0), (int(w / 5) - 1, top_height), ColorList['Red'], 3)
                cv.rectangle(frame, (int(w / 5) + 2, 0), (int(w * 2 / 5) - 1, top_height), ColorList['Green'], 3)
                cv.rectangle(frame, (int(w * 2 / 5) + 2, 0), (int(w * 3 / 5) - 1, top_height), ColorList['Blue'], 3)
                cv.rectangle(frame, (int(w * 3 / 5) + 2, 0), (int(w * 4 / 5) - 1, top_height), ColorList['Yellow'], 3)
                cv.rectangle(frame, (int(w * 4 / 5) + 2, 0), (w - 1, top_height), ColorList['Black'], 3)
            if fingers[1] and fingers[2] == False and math.hypot(x2 - x1, y2 - y1) > 50:
                # print("Drawing mode")
                if xp == yp == 0: xp, yp = x1, y1
                if Color == 'Black':
                    cv.line(frame, (xp, yp), (x1, y1), ColorList[Color], eraserThickness)
                    cv.line(imgCanvas, (xp, yp), (x1, y1), ColorList[Color], eraserThickness)
                else:
                    cv.line(frame, (xp, yp), (x1, y1), ColorList[Color], brushThickness)
                    cv.line(imgCanvas, (xp, yp), (x1, y1), ColorList[Color], brushThickness)
                cv.circle(frame, (x1, y1), 15, ColorList[Color], cv.FILLED)
                xp, yp = x1, y1
            else: xp = yp = 0
        imgGray = cv.cvtColor(imgCanvas, cv.COLOR_BGR2GRAY)
        _, imgInv = cv.threshold(imgGray, 50, 255, cv.THRESH_BINARY_INV)
        imgInv = cv.cvtColor(imgInv, cv.COLOR_GRAY2BGR)
        frame = cv.bitwise_and(frame, imgInv)
        frame = cv.bitwise_or(frame, imgCanvas)
        if cv.waitKey(1) & 0xFF == ord('q'): break
        cTime = time.time()
        fps = 1 / (cTime - pTime)
        pTime = cTime
        text = "FPS : " + str(int(fps))
        cv.rectangle(frame, (20, h - 100), (50, h - 70), ColorList[Color], cv.FILLED)
        cv.putText(frame, Color, (70, h - 75), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)
        cv.putText(frame, text, (20, h-30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)
        cv.imshow('frame', frame)
    capture.release()
    cv.destroyAllWindows()

 

3D object recognition
Finger control

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