OpenCV-Python - Quick Guide
OpenCV (Open Source Computer Vision Library) is one of the most popular libraries for computer vision, machine learning, and image processing. Combined with Python, OpenCV provides powerful tools for building applications that can analyze images, process videos, detect objects, recognize faces, and much more.
This quick guide covers the essential OpenCV-Python concepts and functions that every beginner should know.
What is OpenCV?
OpenCV is an open-source library designed for:
- Image Processing
- Video Processing
- Object Detection
- Face Recognition
- Motion Tracking
- Machine Learning
- Artificial Intelligence Applications
Key advantages include:
- Free and Open Source
- Cross-platform support
- Fast and optimized performance
- Extensive documentation
- Large community support
Installing OpenCV
Install OpenCV using pip:
pip install opencv-python
For additional modules:
pip install opencv-contrib-python
Verify installation:
import cv2
print(cv2.__version__)
Reading an Image
Load an image from disk:
import cv2
img = cv2.imread("image.jpg")
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Writing an Image
Save an image to disk:
cv2.imwrite("output.jpg", img)
Image Properties
Get image dimensions and channels:
print(img.shape)
print(img.size)
print(img.dtype)
Example output:
(720, 1280, 3)
2764800
uint8
Resize an Image
resized = cv2.resize(img, (640, 480))
Rotate an Image
rotated = cv2.rotate(
img,
cv2.ROTATE_90_CLOCKWISE
)
Convert Color Spaces
Convert image to grayscale:
gray = cv2.cvtColor(
img,
cv2.COLOR_BGR2GRAY
)
Convert to HSV:
hsv = cv2.cvtColor(
img,
cv2.COLOR_BGR2HSV
)
Drawing Shapes
Draw a rectangle:
cv2.rectangle(
img,
(50, 50),
(300, 200),
(0, 255, 0),
2
)
Draw a circle:
cv2.circle(
img,
(200, 200),
100,
(255, 0, 0),
3
)
Adding Text
cv2.putText(
img,
"OpenCV",
(50,100),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0,255,0),
2
)
Image Thresholding
gray = cv2.cvtColor(
img,
cv2.COLOR_BGR2GRAY
)
ret, thresh = cv2.threshold(
gray,
127,
255,
cv2.THRESH_BINARY
)
Image Filtering
Gaussian Blur:
blur = cv2.GaussianBlur(
img,
(5,5),
0
)
Median Blur:
median = cv2.medianBlur(
img,
5
)
Edge Detection
Canny Edge Detector:
edges = cv2.Canny(
img,
100,
200
)
Contour Detection
contours, hierarchy = cv2.findContours(
thresh,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE
)
cv2.drawContours(
img,
contours,
-1,
(0,255,0),
2
)
Histogram Calculation
hist = cv2.calcHist(
[img],
[0],
None,
[256],
[0,256]
)
Reading Video Files
cap = cv2.VideoCapture(
"video.mp4"
)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
cv2.imshow("Video", frame)
if cv2.waitKey(25) == 27:
break
cap.release()
cv2.destroyAllWindows()
Capture Video from Camera
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
cv2.imshow("Webcam", frame)
if cv2.waitKey(1) == 27:
break
cap.release()
cv2.destroyAllWindows()
Save Video
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(
'output.avi',
fourcc,
20.0,
(640,480)
)
Face Detection
Load Haar Cascade:
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades +
'haarcascade_frontalface_default.xml'
)
Detect faces:
faces = face_cascade.detectMultiScale(
gray,
1.1,
5
)
Draw detection boxes:
for (x,y,w,h) in faces:
cv2.rectangle(
img,
(x,y),
(x+w,y+h),
(0,255,0),
2
)
Feature Detection with ORB
orb = cv2.ORB_create()
kp, des = orb.detectAndCompute(
gray,
None
)
Draw keypoints:
result = cv2.drawKeypoints(
img,
kp,
None
)
Feature Matching
bf = cv2.BFMatcher(
cv2.NORM_HAMMING,
crossCheck=True
)
matches = bf.match(
des1,
des2
)
Template Matching
result = cv2.matchTemplate(
image,
template,
cv2.TM_CCOEFF_NORMED
)
Morphological Transformations
Erosion:
erosion = cv2.erode(
img,
kernel,
iterations=1
)
Dilation:
dilation = cv2.dilate(
img,
kernel,
iterations=1
)
Fourier Transform
Convert image into frequency domain:
dft = cv2.dft(
np.float32(gray),
flags=cv2.DFT_COMPLEX_OUTPUT
)
Image Pyramids
Downsample image:
lower = cv2.pyrDown(img)
Upsample image:
higher = cv2.pyrUp(lower)
Machine Learning with KNN
Create KNN classifier:
knn = cv2.ml.KNearest_create()
Train model:
knn.train(
trainData,
cv2.ml.ROW_SAMPLE,
responses
)
Common OpenCV Applications
OpenCV is widely used in:
- Face Recognition
- Object Detection
- License Plate Recognition
- Medical Imaging
- Robotics
- Autonomous Vehicles
- Augmented Reality
- Surveillance Systems
- OCR Applications
- Gesture Recognition
Best Practices
Use Grayscale When Possible
Reduces processing time.
gray = cv2.cvtColor(
img,
cv2.COLOR_BGR2GRAY
)
Resize Large Images
Improves performance.
Release Resources
Always release camera and video objects.
cap.release()
cv2.destroyAllWindows()
Handle Errors
Check if images or videos load correctly.
if img is None:
print("Image not found")
OpenCV Learning Roadmap
Beginner Level
- Reading Images
- Writing Images
- Drawing Shapes
- Thresholding
- Filtering
Intermediate Level
- Contours
- Histograms
- Video Processing
- Face Detection
- Feature Detection
Advanced Level
- Object Tracking
- Image Stitching
- Machine Learning
- Deep Learning
- Real-Time AI Applications
Conclusion
OpenCV-Python is one of the most powerful and beginner-friendly libraries for computer vision and image processing. From basic image manipulation to advanced AI-powered applications, OpenCV provides everything needed to build professional computer vision projects.
Master the fundamentals covered in this quick guide, and you'll be ready to explore advanced topics such as object detection, facial recognition, deep learning, image segmentation, and real-time computer vision systems.
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