This is a Computer vision package that makes its easy to run Image processing and AI functions. At the core it uses OpenCV and Mediapipe libraries.
You can simply use pip to install the latest version of cvzone.
pip install cvzone
For sample usage and examples, please refer to the Examples folder in this repository. This folder contains various examples to help you understand how to make the most out of cvzone's features.
- Installations
- Corner Rectangle
- PutTextRect
- Download Image from URL
- Overlay PNG
- Rotate Image
- Stack Images
- FPS
- Finding Contours
- Color Module
- Classification Module
- Face Detection
- Face Mesh Module
- Selfie Segmentation Module
- Hand Tracking Module
- Pose Module
- Serial Module
- Plot Module
To install the cvzone package, run the following command:
pip install cvzone
import cv2
import cvzone # Importing the cvzone library
# Initialize the webcam
cap = cv2.VideoCapture(2) # Capture video from the third webcam (0-based index)
# Main loop to continuously capture frames
while True:
# Capture a single frame from the webcam
success, 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 image
img = 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 edges
t=5, # Thickness of the corner edges
rt=1, # Thickness of the rectangle
colorR=(255, 0, 255), # Color of the rectangle
colorC=(0, 255, 0) # Color of the corner edges
)
# Show the modified image
cv2.imshow("Image", img) # Display the image in a window named "Image"
# Wait for 1 millisecond between frames
cv2.waitKey(1) # Waits 1 ms for a key event (not being used here)
import cv2
import cvzone # Importing the cvzone library
# Initialize the webcam
cap = cv2.VideoCapture(2) # Capture video from the third webcam (0-based index)
# Main loop to continuously capture frames
while True:
# Capture a single frame from the webcam
success, 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 image
img, bbox = cvzone.putTextRect(
img, "CVZone", (50, 50), # Image and starting position of the rectangle
scale=3, thickness=3, # Font scale and thickness
colorT=(255, 255, 255), colorR=(255, 0, 255), # Text color and Rectangle color
font=cv2.FONT_HERSHEY_PLAIN, # Font type
offset=10, # Offset of text inside the rectangle
border=5, colorB=(0, 255, 0) # Border thickness and color
)
# Show the modified image
cv2.imshow("Image", img) # Display the image in a window named "Image"
# Wait for 1 millisecond between frames
cv2.waitKey(1) # Waits 1 ms for a key event (not being used here)import cv2
import cvzone
imgNormal = 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)
import cv2
import cvzone
# Initialize camera capture
cap = 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)
while True:
# Read image frame from camera
success, 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)
import cv2
from cvzone.Utils import rotateImage # Import rotateImage function from cvzone.Utils
# Initialize the video capture
cap = cv2.VideoCapture(2) # Capture video from the third webcam (index starts at 0)
# Start the loop to continuously get frames from the webcam
while True:
# Read a frame from the webcam
success, 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 size
imgRotated60 = 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 size
imgRotated60KeepSize = rotateImage(img, 60, scale=1,
keepSize=True) # Rotate image 60 degrees, scale it by 1, and keep the original size
# Display the rotated images
cv2.imshow("imgRotated60", imgRotated60) # Show the 60-degree rotated image without keeping the size
cv2.imshow("imgRotated60KeepSize", imgRotated60KeepSize) # Show the 60-degree rotated image while keeping the size
# Wait for 1 millisecond between frames
cv2.waitKey(1) # Wait for 1 ms, during which any key press can be detected (not being used here)
import cv2
import cvzone
# Initialize camera capture
cap = cv2.VideoCapture(2)
# Start an infinite loop to continually capture frames
while True:
# Read image frame from camera
success, img = cap.read()
# Convert the image to grayscale
imgGray = 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 image
imgCanny = cv2.Canny(imgGray, 50, 150)
# Convert the image to HSV color space
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Create a list of all processed images
imgList = [img, imgGray, imgCanny, imgSmall, imgBig, imgHSV]
# Stack the images together using cvzone's stackImages function
stackedImg = cvzone.stackImages(imgList, 3, 0.7)
# Display the stacked images
cv2.imshow("stackedImg", stackedImg)
# Wait for 1 millisecond; this also allows for keyboard inputs
cv2.waitKey(1)import cvzone
import cv2
# Initialize the FPS class with an average count of 30 frames for smoothing
fpsReader = cvzone.FPS(avgCount=30)
# Initialize the webcam and set it to capture
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 30) # Set the frames per second to 30
# Main loop to capture frames and display FPS
while True:
# Read a frame from the webcam
success, img = cap.read()
# Update the FPS counter and draw the FPS on the image
# fpsReader.update returns the current FPS and the updated image
fps, 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 counter
cv2.imshow("Image", img)
# Wait for 1 ms to show this frame, then continue to the next frame
cv2.waitKey(1)import cv2 # Importing the OpenCV library for computer vision tasks
import cvzone # Importing the cvzone library for additional functionalities
import numpy as np # Importing NumPy library for numerical operations
# Download an image containing shapes from a given URL
imgShapes = cvzone.downloadImageFromUrl(
url='https://github.com/cvzone/cvzone/blob/master/Results/shapes.png?raw=true')
# Perform edge detection using the Canny algorithm
imgCanny = cv2.Canny(imgShapes, 50, 150)
# Dilate the edges to strengthen the detected contours
imgDilated = cv2.dilate(imgCanny, np.ones((5, 5), np.uint8), iterations=1)
# Find contours in the image without any corner filtering
imgContours, 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 contours
cv2.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 windows
cv2.waitKey(0)import cvzone
import cv2
# 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.
while True:
# 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.
if cv2.waitKey(1) & 0xFF == ord('q'):
breakfrom cvzone.ClassificationModule import Classifier
import cv2
cap = cv2.VideoCapture(2) # Initialize video capture
path = "C:/Users/USER/Documents/maskModel/"
maskClassifier = Classifier(f'{path}/keras_model.h5', f'{path}/labels.txt')
while True:
_, img = cap.read() # Capture frame-by-frame
prediction = maskClassifier.getPrediction(img)
print(prediction) # Print prediction result
cv2.imshow("Image", img)
cv2.waitKey(1) # Wait for a key pressimport cvzone
from cvzone.FaceDetectionModule import FaceDetector
import cv2
# Initialize the webcam
# '2' means the third camera connected to the computer, usually 0 refers to the built-in webcam
cap = 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 webcam
while True:
# Read the current frame from the webcam
# success: Boolean, whether the frame was successfully grabbed
# img: the captured frame
success, img = cap.read()
# Detect faces in the image
# img: Updated image
# bboxs: List of bounding boxes around detected faces
img, bboxs = detector.findFaces(img, draw=False)
# Check if any face is detected
if bboxs:
# Loop through each bounding box
for bbox in bboxs:
# 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 open
cv2.waitKey(1)from cvzone.FaceMeshModule import FaceMeshDetector
import cv2
# Initialize the webcam
# '2' indicates the third camera connected to the computer, '0' would usually refer to the built-in webcam
cap = 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 threshold
detector = FaceMeshDetector(staticMode=False, maxFaces=2, minDetectionCon=0.5, minTrackCon=0.5)
# Start the loop to continually get frames from the webcam
while True:
# Read the current frame from the webcam
# success: Boolean, whether the frame was successfully grabbed
# img: The current frame
success, img = cap.read()
# Find face mesh in the image
# img: Updated image with the face mesh if draw=True
# faces: Detected face information
img, faces = detector.findFaceMesh(img, draw=True)
# Check if any faces are detected
if faces:
# Loop through each detected face
for face in faces:
# Get specific points for the eye
# leftEyeUpPoint: Point above the left eye
# leftEyeDownPoint: Point below the left eye
leftEyeUpPoint = 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 information
print(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 open
cv2.waitKey(1)import cvzone
from cvzone.SelfiSegmentationModule import SelfiSegmentation
import cv2
# 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 pixels
cap.set(3, 640)
# Set the frame height to 480 pixels
cap.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 webcam
while True:
# Capture a single frame
success, 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 side
imgStacked = cvzone.stackImages([img, imgOut], cols=2, scale=1)
# Display the stacked images
cv2.imshow("Image", imgStacked)
# Check for 'q' key press to break the loop and close the window
if cv2.waitKey(1) & 0xFF == ord('q'):
breakfrom cvzone.HandTrackingModule import HandDetector
import cv2
# Initialize the webcam to capture video
# The '2' indicates the third camera connected to your computer; '0' would usually refer to the built-in camera
cap = cv2.VideoCapture(2)
# Initialize the HandDetector class with the given parameters
detector = HandDetector(staticMode=False, maxHands=2, modelComplexity=1, detectionCon=0.5, minTrackCon=0.5)
# Continuously get frames from the webcam
while True:
# Capture each frame from the webcam
# 'success' will be True if the frame is successfully captured, 'img' will contain the frame
success, 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 detections
hands, img = detector.findHands(img, draw=True, flipType=True)
# Check if any hands are detected
if hands:
# Information for the first hand detected
hand1 = hands[0] # Get the first hand detected
lmList1 = hand1["lmList"] # List of 21 landmarks for the first hand
bbox1 = hand1["bbox"] # Bounding box around the first hand (x,y,w,h coordinates)
center1 = hand1['center'] # Center coordinates of the first hand
handType1 = hand1["type"] # Type of the first hand ("Left" or "Right")
# Count the number of fingers up for the first hand
fingers1 = 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 image
length, 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 detected
if len(hands) == 2:
# Information for the second hand
hand2 = hands[1]
lmList2 = hand2["lmList"]
bbox2 = hand2["bbox"]
center2 = hand2['center']
handType2 = hand2["type"]
# Count the number of fingers up for the second hand
fingers2 = detector.fingersUp(hand2)
print(f'H2 = {fingers2.count(1)}', end=" ")
# Calculate distance between the index fingers of both hands and draw it on the image
length, 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 window
cv2.imshow("Image", img)
# Keep the window open and update it for each frame; wait for 1 millisecond between frames
cv2.waitKey(1)from cvzone.PoseModule import PoseDetector
import cv2
# Initialize the webcam and set it to the third camera (index 2)
cap = cv2.VideoCapture(2)
# Initialize the PoseDetector class with the given parameters
detector = PoseDetector(staticMode=False,
modelComplexity=1,
smoothLandmarks=True,
enableSegmentation=False,
smoothSegmentation=True,
detectionCon=0.5,
trackCon=0.5)
# Loop to continuously get frames from the webcam
while True:
# Capture each frame from the webcam
success, img = cap.read()
# Find the human pose in the frame
img = 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 image
lmList, bboxInfo = detector.findPosition(img, draw=True, bboxWithHands=False)
# Check if any body landmarks are detected
if lmList:
# Get the center of the bounding box around the body
center = bboxInfo["center"]
# Draw a circle at the center of the bounding box
cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
# Calculate the distance between landmarks 11 and 15 and draw it on the image
length, 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 image
angle, 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 10
isCloseAngle50 = detector.angleCheck(myAngle=angle,
targetAngle=50,
offset=10)
# Print the result of the angle check
print(isCloseAngle50)
# Display the frame in a window
cv2.imshow("Image", img)
# Wait for 1 millisecond between each frame
cv2.waitKey(1)from cvzone.SerialModule import SerialObject
# Initialize the Arduino SerialObject with optional parameters
# baudRate = 9600, digits = 1, max_retries = 5
arduino = SerialObject(portNo=None, baudRate=9600, digits=1, max_retries=5)
# Initialize a counter to keep track of iterations
count = 0
# Start an infinite loop
while True:
# Increment the counter on each iteration
count += 1
# Print data received from the Arduino
# getData method returns the list of data received from the Arduino
print(arduino.getData())
# If the count is less than 100
if count < 100:
# Send a list containing [1] to the Arduino
arduino.sendData([1])
else:
# If the count is 100 or greater, send a list containing [0] to the Arduino
arduino.sendData([0])
# Reset the count back to 0 once it reaches 200
# This will make the cycle repeat
if count == 200:
count = 0#include <cvzone.h>
SerialData serialData(1,1); //(numOfValsRec,digitsPerValRec)
/*0 or 1 - 1 digit
0 to 99 - 2 digits
0 to 999 - 3 digits
*/
//SerialData serialData; // if not receving only sending
int sendVals[2]; // min val of 2 even when sending 1
int valsRec[1];
int x = 0;
void setup() {
serialData.begin(9600);
pinMode(13,OUTPUT);
}
void loop() {
// ------- To SEND --------
x +=1;
if (x==100){x=0;}
sendVals[0] = x;
serialData.Send(sendVals);
// ------- To Recieve --------
serialData.Get(valsRec);
digitalWrite(13,valsRec[0]);
}from cvzone.PlotModule import LivePlot
import cv2
import math
sinPlot = LivePlot(w=1200, yLimit=[-100, 100], interval=0.01)
xSin=0
while True:
xSin += 1
if xSin == 360: xSin = 0
imgPlotSin = sinPlot.update(int(math.sin(math.radians(xSin)) * 100))
cv2.imshow("Image Sin Plot", imgPlotSin)
if cv2.waitKey(1) & 0xFF == ord('q'):
breakfrom cvzone.PlotModule import LivePlot
from cvzone.FaceDetectionModule import FaceDetector
import cv2
import cvzone
cap = cv2.VideoCapture(1)
detector = FaceDetector(minDetectionCon=0.85, modelSelection=0)
xPlot = LivePlot(w=1200, yLimit=[0, 500], interval=0.01)
while True:
success, img = cap.read()
img, bboxs = detector.findFaces(img, draw=False)
val = 0
if bboxs:
# Loop through each bounding box
for bbox in bboxs:
# 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)
if cv2.waitKey(1) & 0xFF == ord('q'):
break