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Perform filtering, transformations, edge detection, and morphological operations with ease.
Optimized for speed with support for hardware acceleration and multi-threading.
Runs on Windows, Linux, macOS, Android, and iOS with bindings for multiple languages.
Integrates with deep learning frameworks like TensorFlow, PyTorch, and ONNX for advanced tasks.
Use pip (`opencv-python`) or build from source for full feature support.
Use `cv2.imread()` or `cv2.VideoCapture()` to load media for processing.
Use functions like `cv2.resize()`, `cv2.cvtColor()`, and `cv2.GaussianBlur()` to manipulate images.
Use Haar cascades, DNN modules, or contour detection for object and face recognition.
Use `cv2.imshow()` to visualize and `cv2.imwrite()` to save processed outputs.
import cv2
# Load image
img = cv2.imread("sample.jpg")
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Detect edges
edges = cv2.Canny(blurred, 50, 150)
# Show result
cv2.imshow("Edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()Detect and track faces in images or video streams using Haar cascades or DNNs.
Track moving objects across frames using optical flow or tracking algorithms.
Apply blurs, sharpening, thresholding, and other filters for preprocessing.
Calibrate cameras and correct lens distortion for accurate measurements.
Explore OpenCV’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about OpenCV’s capabilities, usage, and ecosystem.