importcv2importcvzone# Importing the cvzone library# Initialize the webcamcap=cv2.VideoCapture(2) # Capture video from the third webcam (0-based index)# Main loop to continuously capture frameswhileTrue:
# Capture a single frame from the webcamsuccess, img=cap.read() # 'success' is a boolean that indicates if the frame was captured successfully, and 'img' contains the frame itself# Add a rectangle with styled corners to the imageimg=cvzone.cornerRect(
img, # The image to draw on
(200, 200, 300, 200), # The position and dimensions of the rectangle (x, y, width, height)l=30, # Length of the corner edgest=5, # Thickness of the corner edgesrt=1, # Thickness of the rectanglecolorR=(255, 0, 255), # Color of the rectanglecolorC=(0, 255, 0) # Color of the corner edges
)
# Show the modified imagecv2.imshow("Image", img) # Display the image in a window named "Image"# Wait for 1 millisecond between framescv2.waitKey(1) # Waits 1 ms for a key event (not being used here)

importcv2importcvzone# Importing the cvzone library# Initialize the webcamcap=cv2.VideoCapture(2) # Capture video from the third webcam (0-based index)# Main loop to continuously capture frameswhileTrue:
# Capture a single frame from the webcamsuccess, img=cap.read() # 'success' is a boolean that indicates if the frame was captured successfully, and 'img' contains the frame itself# Add a rectangle and put text inside it on the imageimg, bbox=cvzone.putTextRect(
img, "CVZone", (50, 50), # Image and starting position of the rectanglescale=3, thickness=3, # Font scale and thicknesscolorT=(255, 255, 255), colorR=(255, 0, 255), # Text color and Rectangle colorfont=cv2.FONT_HERSHEY_PLAIN, # Font typeoffset=10, # Offset of text inside the rectangleborder=5, colorB=(0, 255, 0) # Border thickness and color
)
# Show the modified imagecv2.imshow("Image", img) # Display the image in a window named "Image"# Wait for 1 millisecond between framescv2.waitKey(1) # Waits 1 ms for a key event (not being used here)

importcv2importcvzoneimgNormal=cvzone.downloadImageFromUrl(
url='https://github.com/cvzone/cvzone/blob/master/Results/shapes.png?raw=true')
imgPNG=cvzone.downloadImageFromUrl(
url='https://github.com/cvzone/cvzone/blob/master/Results/cvzoneLogo.png?raw=true',
keepTransparency=True)
imgPNG=cv2.resize(imgPNG,(0,0),None,3,3)
cv2.imshow("Image Normal", imgNormal)
cv2.imshow("Transparent Image", imgPNG)
cv2.waitKey(0)

importcv2importcvzone# Initialize camera capturecap=cv2.VideoCapture(2)
# imgPNG = cvzone.downloadImageFromUrl(# url='https://github.com/cvzone/cvzone/blob/master/Results/cvzoneLogo.png?raw=true',# keepTransparency=True)imgPNG=cv2.imread("cvzoneLogo.png",cv2.IMREAD_UNCHANGED)
whileTrue:
# Read image frame from camerasuccess, img=cap.read()
imgOverlay=cvzone.overlayPNG(img, imgPNG, pos=[-30, 50])
imgOverlay=cvzone.overlayPNG(img, imgPNG, pos=[200, 200])
imgOverlay=cvzone.overlayPNG(img, imgPNG, pos=[500, 400])
cv2.imshow("imgOverlay", imgOverlay)
cv2.waitKey(1)

importcv2fromcvzone.UtilsimportrotateImage# Import rotateImage function from cvzone.Utils# Initialize the video capturecap=cv2.VideoCapture(2) # Capture video from the third webcam (index starts at 0)# Start the loop to continuously get frames from the webcamwhileTrue:
# Read a frame from the webcamsuccess, img=cap.read() # 'success' will be True if the frame is read successfully, 'img' will contain the frame# Rotate the image by 60 degrees without keeping the sizeimgRotated60=rotateImage(img, 60, scale=1,
keepSize=False) # Rotate image 60 degrees, scale it by 1, and don't keep original size# Rotate the image by 60 degrees while keeping the sizeimgRotated60KeepSize=rotateImage(img, 60, scale=1,
keepSize=True) # Rotate image 60 degrees, scale it by 1, and keep the original size# Display the rotated imagescv2.imshow("imgRotated60", imgRotated60) # Show the 60-degree rotated image without keeping the sizecv2.imshow("imgRotated60KeepSize", imgRotated60KeepSize) # Show the 60-degree rotated image while keeping the size# Wait for 1 millisecond between framescv2.waitKey(1) # Wait for 1 ms, during which any key press can be detected (not being used here)

importcv2importcvzone# Initialize camera capturecap=cv2.VideoCapture(2)
# Start an infinite loop to continually capture frameswhileTrue:
# Read image frame from camerasuccess, img=cap.read()
# Convert the image to grayscaleimgGray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Resize the image to be smaller (0.1x of original size)imgSmall=cv2.resize(img, (0, 0), None, 0.1, 0.1)
# Resize the image to be larger (3x of original size)imgBig=cv2.resize(img, (0, 0), None, 3, 3)
# Apply Canny edge detection on the grayscale imageimgCanny=cv2.Canny(imgGray, 50, 150)
# Convert the image to HSV color spaceimgHSV=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Create a list of all processed imagesimgList= [img, imgGray, imgCanny, imgSmall, imgBig, imgHSV]
# Stack the images together using cvzone's stackImages functionstackedImg=cvzone.stackImages(imgList, 3, 0.7)
# Display the stacked imagescv2.imshow("stackedImg", stackedImg)
# Wait for 1 millisecond; this also allows for keyboard inputscv2.waitKey(1)

importcvzoneimportcv2# Initialize the FPS class with an average count of 30 frames for smoothingfpsReader=cvzone.FPS(avgCount=30)
# Initialize the webcam and set it to capturecap=cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 30) # Set the frames per second to 30# Main loop to capture frames and display FPSwhileTrue:
# Read a frame from the webcamsuccess, img=cap.read()
# Update the FPS counter and draw the FPS on the image# fpsReader.update returns the current FPS and the updated imagefps, img=fpsReader.update(img, pos=(20, 50),
bgColor=(255, 0, 255), textColor=(255, 255, 255),
scale=3, thickness=3)
# Display the image with the FPS countercv2.imshow("Image", img)
# Wait for 1 ms to show this frame, then continue to the next framecv2.waitKey(1)

importcv2# Importing the OpenCV library for computer vision tasksimportcvzone# Importing the cvzone library for additional functionalitiesimportnumpyasnp# Importing NumPy library for numerical operations# Download an image containing shapes from a given URLimgShapes=cvzone.downloadImageFromUrl(
url='https://github.com/cvzone/cvzone/blob/master/Results/shapes.png?raw=true')
# Perform edge detection using the Canny algorithmimgCanny=cv2.Canny(imgShapes, 50, 150)
# Dilate the edges to strengthen the detected contoursimgDilated=cv2.dilate(imgCanny, np.ones((5, 5), np.uint8), iterations=1)
# Find contours in the image without any corner filteringimgContours, conFound=cvzone.findContours(
imgShapes, imgDilated, minArea=1000, sort=True,
filter=None, drawCon=True, c=(255, 0, 0), ct=(255, 0, 255),
retrType=cv2.RETR_EXTERNAL, approxType=cv2.CHAIN_APPROX_NONE)
# Find contours in the image and filter them based on corner points (either 3 or 4 corners)imgContoursFiltered, conFoundFiltered=cvzone.findContours(
imgShapes, imgDilated, minArea=1000, sort=True,
filter=[3, 4], drawCon=True, c=(255, 0, 0), ct=(255, 0, 255),
retrType=cv2.RETR_EXTERNAL, approxType=cv2.CHAIN_APPROX_NONE)
# Display the image with all found contourscv2.imshow("imgContours", imgContours)
# Display the image with filtered contours (either 3 or 4 corners)cv2.imshow("imgContoursFiltered", imgContoursFiltered)
# Wait until a key is pressed to close the windowscv2.waitKey(0)

importcvzoneimportcv2# Create an instance of the ColorFinder class with trackBar set to True.myColorFinder=cvzone.ColorFinder(trackBar=True)
# Initialize the video capture using OpenCV.# Using the third camera (index 2). Adjust index if you have multiple cameras.cap=cv2.VideoCapture(2)
# Set the dimensions of the camera feed to 640x480.cap.set(3, 640)
cap.set(4, 480)
# Custom color values for detecting orange.# 'hmin', 'smin', 'vmin' are the minimum values for Hue, Saturation, and Value.# 'hmax', 'smax', 'vmax' are the maximum values for Hue, Saturation, and Value.hsvVals= {'hmin': 10, 'smin': 55, 'vmin': 215, 'hmax': 42, 'smax': 255, 'vmax': 255}
# Main loop to continuously get frames from the camera.whileTrue:
# Read the current frame from the camera.success, img=cap.read()
# Use the update method from the ColorFinder class to detect the color.# It returns the masked color image and a binary mask.imgOrange, mask=myColorFinder.update(img, hsvVals)
# Stack the original image, the masked color image, and the binary mask.imgStack=cvzone.stackImages([img, imgOrange, mask], 3, 1)
# Show the stacked images.cv2.imshow("Image Stack", imgStack)
# Break the loop if the 'q' key is pressed.ifcv2.waitKey(1) &0xFF==ord('q'):
break

fromcvzone.ClassificationModuleimportClassifierimportcv2cap=cv2.VideoCapture(2) # Initialize video capturepath="C:/Users/USER/Documents/maskModel/"maskClassifier=Classifier(f'{path}/keras_model.h5', f'{path}/labels.txt')
whileTrue:
_, img=cap.read() # Capture frame-by-frameprediction=maskClassifier.getPrediction(img)
print(prediction) # Print prediction resultcv2.imshow("Image", img)
cv2.waitKey(1) # Wait for a key press

importcvzonefromcvzone.FaceDetectionModuleimportFaceDetectorimportcv2# Initialize the webcam# '2' means the third camera connected to the computer, usually 0 refers to the built-in webcamcap=cv2.VideoCapture(2)
# Initialize the FaceDetector object# minDetectionCon: Minimum detection confidence threshold# modelSelection: 0 for short-range detection (2 meters), 1 for long-range detection (5 meters)detector=FaceDetector(minDetectionCon=0.5, modelSelection=0)
# Run the loop to continually get frames from the webcamwhileTrue:
# Read the current frame from the webcam# success: Boolean, whether the frame was successfully grabbed# img: the captured framesuccess, img=cap.read()
# Detect faces in the image# img: Updated image# bboxs: List of bounding boxes around detected facesimg, bboxs=detector.findFaces(img, draw=False)
# Check if any face is detectedifbboxs:
# Loop through each bounding boxforbboxinbboxs:
# bbox contains 'id', 'bbox', 'score', 'center'# ---- Get Data ---- #center=bbox["center"]
x, y, w, h=bbox['bbox']
score=int(bbox['score'][0] *100)
# ---- Draw Data ---- #cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
cvzone.putTextRect(img, f'{score}%', (x, y-10))
cvzone.cornerRect(img, (x, y, w, h))
# Display the image in a window named 'Image'cv2.imshow("Image", img)
# Wait for 1 millisecond, and keep the window opencv2.waitKey(1)

fromcvzone.FaceMeshModuleimportFaceMeshDetectorimportcv2# Initialize the webcam# '2' indicates the third camera connected to the computer, '0' would usually refer to the built-in webcamcap=cv2.VideoCapture(2)
# Initialize FaceMeshDetector object# staticMode: If True, the detection happens only once, else every frame# maxFaces: Maximum number of faces to detect# minDetectionCon: Minimum detection confidence threshold# minTrackCon: Minimum tracking confidence thresholddetector=FaceMeshDetector(staticMode=False, maxFaces=2, minDetectionCon=0.5, minTrackCon=0.5)
# Start the loop to continually get frames from the webcamwhileTrue:
# Read the current frame from the webcam# success: Boolean, whether the frame was successfully grabbed# img: The current framesuccess, img=cap.read()
# Find face mesh in the image# img: Updated image with the face mesh if draw=True# faces: Detected face informationimg, faces=detector.findFaceMesh(img, draw=True)
# Check if any faces are detectediffaces:
# Loop through each detected faceforfaceinfaces:
# Get specific points for the eye# leftEyeUpPoint: Point above the left eye# leftEyeDownPoint: Point below the left eyeleftEyeUpPoint=face[159]
leftEyeDownPoint=face[23]
# Calculate the vertical distance between the eye points# leftEyeVerticalDistance: Distance between points above and below the left eye# info: Additional information (like coordinates)leftEyeVerticalDistance, info=detector.findDistance(leftEyeUpPoint, leftEyeDownPoint)
# Print the vertical distance for debugging or informationprint(leftEyeVerticalDistance)
# Display the image in a window named 'Image'cv2.imshow("Image", img)
# Wait for 1 millisecond to check for any user input, keeping the window opencv2.waitKey(1)

importcvzonefromcvzone.SelfiSegmentationModuleimportSelfiSegmentationimportcv2# Initialize the webcam. '2' indicates the third camera connected to the computer.# '0' usually refers to the built-in camera.cap=cv2.VideoCapture(2)
# Set the frame width to 640 pixelscap.set(3, 640)
# Set the frame height to 480 pixelscap.set(4, 480)
# Initialize the SelfiSegmentation class. It will be used for background removal.# model is 0 or 1 - 0 is general 1 is landscape(faster)segmentor=SelfiSegmentation(model=0)
# Infinite loop to keep capturing frames from the webcamwhileTrue:
# Capture a single framesuccess, img=cap.read()
# Use the SelfiSegmentation class to remove the background# Replace it with a magenta background (255, 0, 255)# imgBG can be a color or an image as well. must be same size as the original if image# 'cutThreshold' is the sensitivity of the segmentation.imgOut=segmentor.removeBG(img, imgBg=(255, 0, 255), cutThreshold=0.1)
# Stack the original image and the image with background removed side by sideimgStacked=cvzone.stackImages([img, imgOut], cols=2, scale=1)
# Display the stacked imagescv2.imshow("Image", imgStacked)
# Check for 'q' key press to break the loop and close the windowifcv2.waitKey(1) &0xFF==ord('q'):
break

fromcvzone.HandTrackingModuleimportHandDetectorimportcv2# Initialize the webcam to capture video# The '2' indicates the third camera connected to your computer; '0' would usually refer to the built-in cameracap=cv2.VideoCapture(2)
# Initialize the HandDetector class with the given parametersdetector=HandDetector(staticMode=False, maxHands=2, modelComplexity=1, detectionCon=0.5, minTrackCon=0.5)
# Continuously get frames from the webcamwhileTrue:
# Capture each frame from the webcam# 'success' will be True if the frame is successfully captured, 'img' will contain the framesuccess, img=cap.read()
# Find hands in the current frame# The 'draw' parameter draws landmarks and hand outlines on the image if set to True# The 'flipType' parameter flips the image, making it easier for some detectionshands, img=detector.findHands(img, draw=True, flipType=True)
# Check if any hands are detectedifhands:
# Information for the first hand detectedhand1=hands[0] # Get the first hand detectedlmList1=hand1["lmList"] # List of 21 landmarks for the first handbbox1=hand1["bbox"] # Bounding box around the first hand (x,y,w,h coordinates)center1=hand1['center'] # Center coordinates of the first handhandType1=hand1["type"] # Type of the first hand ("Left" or "Right")# Count the number of fingers up for the first handfingers1=detector.fingersUp(hand1)
print(f'H1 = {fingers1.count(1)}', end=" ") # Print the count of fingers that are up# Calculate distance between specific landmarks on the first hand and draw it on the imagelength, info, img=detector.findDistance(lmList1[8][0:2], lmList1[12][0:2], img, color=(255, 0, 255),
scale=10)
# Check if a second hand is detectediflen(hands) ==2:
# Information for the second handhand2=hands[1]
lmList2=hand2["lmList"]
bbox2=hand2["bbox"]
center2=hand2['center']
handType2=hand2["type"]
# Count the number of fingers up for the second handfingers2=detector.fingersUp(hand2)
print(f'H2 = {fingers2.count(1)}', end=" ")
# Calculate distance between the index fingers of both hands and draw it on the imagelength, info, img=detector.findDistance(lmList1[8][0:2], lmList2[8][0:2], img, color=(255, 0, 0),
scale=10)
print(" ") # New line for better readability of the printed output# Display the image in a windowcv2.imshow("Image", img)
# Keep the window open and update it for each frame; wait for 1 millisecond between framescv2.waitKey(1)

fromcvzone.PoseModuleimportPoseDetectorimportcv2# Initialize the webcam and set it to the third camera (index 2)cap=cv2.VideoCapture(2)
# Initialize the PoseDetector class with the given parametersdetector=PoseDetector(staticMode=False,
modelComplexity=1,
smoothLandmarks=True,
enableSegmentation=False,
smoothSegmentation=True,
detectionCon=0.5,
trackCon=0.5)
# Loop to continuously get frames from the webcamwhileTrue:
# Capture each frame from the webcamsuccess, img=cap.read()
# Find the human pose in the frameimg=detector.findPose(img)
# Find the landmarks, bounding box, and center of the body in the frame# Set draw=True to draw the landmarks and bounding box on the imagelmList, bboxInfo=detector.findPosition(img, draw=True, bboxWithHands=False)
# Check if any body landmarks are detectediflmList:
# Get the center of the bounding box around the bodycenter=bboxInfo["center"]
# Draw a circle at the center of the bounding boxcv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
# Calculate the distance between landmarks 11 and 15 and draw it on the imagelength, img, info=detector.findDistance(lmList[11][0:2],
lmList[15][0:2],
img=img,
color=(255, 0, 0),
scale=10)
# Calculate the angle between landmarks 11, 13, and 15 and draw it on the imageangle, img=detector.findAngle(lmList[11][0:2],
lmList[13][0:2],
lmList[15][0:2],
img=img,
color=(0, 0, 255),
scale=10)
# Check if the angle is close to 50 degrees with an offset of 10isCloseAngle50=detector.angleCheck(myAngle=angle,
targetAngle=50,
offset=10)
# Print the result of the angle checkprint(isCloseAngle50)
# Display the frame in a windowcv2.imshow("Image", img)
# Wait for 1 millisecond between each framecv2.waitKey(1)

fromcvzone.SerialModuleimportSerialObject# Initialize the Arduino SerialObject with optional parameters# baudRate = 9600, digits = 1, max_retries = 5arduino=SerialObject(portNo=None, baudRate=9600, digits=1, max_retries=5)
# Initialize a counter to keep track of iterationscount=0# Start an infinite loopwhileTrue:
# Increment the counter on each iterationcount+=1# Print data received from the Arduino# getData method returns the list of data received from the Arduinoprint(arduino.getData())
# If the count is less than 100ifcount<100:
# Send a list containing [1] to the Arduinoarduino.sendData([1])
else:
# If the count is 100 or greater, send a list containing [0] to the Arduinoarduino.sendData([0])
# Reset the count back to 0 once it reaches 200# This will make the cycle repeatifcount==200:
count=0

fromcvzone.PlotModuleimportLivePlotimportcv2importmathsinPlot=LivePlot(w=1200, yLimit=[-100, 100], interval=0.01)
xSin=0whileTrue:
xSin+=1ifxSin==360: xSin=0imgPlotSin=sinPlot.update(int(math.sin(math.radians(xSin)) *100))
cv2.imshow("Image Sin Plot", imgPlotSin)
ifcv2.waitKey(1) &0xFF==ord('q'):
break

fromcvzone.PlotModuleimportLivePlotfromcvzone.FaceDetectionModuleimportFaceDetectorimportcv2importcvzonecap=cv2.VideoCapture(1)
detector=FaceDetector(minDetectionCon=0.85, modelSelection=0)
xPlot=LivePlot(w=1200, yLimit=[0, 500], interval=0.01)
whileTrue:
success, img=cap.read()
img, bboxs=detector.findFaces(img, draw=False)
val=0ifbboxs:
# Loop through each bounding boxforbboxinbboxs:
# bbox contains 'id', 'bbox', 'score', 'center'# ---- Get Data ---- #center=bbox["center"]
x, y, w, h=bbox['bbox']
score=int(bbox['score'][0] *100)
val=center[0]
# ---- Draw Data ---- #cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
cvzone.putTextRect(img, f'{score}%', (x, y-10))
cvzone.cornerRect(img, (x, y, w, h))
imgPlot=xPlot.update(val)
cv2.imshow("Image Plot", imgPlot)
cv2.imshow("Image", img)
ifcv2.waitKey(1) &0xFF==ord('q'):
break