OpenCV with Python (Computer Vision)

Learn to read and save images with OpenCV in Python, using image path and flags to obtain color, grayscale, or alpha channels, then display the image.

Learn how to crop an image in OpenCV with Python by reading the image using cv2.imread, selecting coordinates x1, x2, y1, y2, slicing image[y1:y2, x1:x2], and displaying the cropped image.

Resize images to a fixed size for deep learning models using cv2.resize. Read the image with cv2.imread, then display the resized image with cv2.imshow and close with cv2.waitKey(0) and cv2.destroyAllWindows.

Learn to rotate images with OpenCV using cv2.rotate, performing 90 degree clockwise, 180 degree, and 270 degree rotations, by reading with cv2.imread and displaying with cv2.imshow.

Learn to split a bgr image into blue, green, and red channels using cv2.split, read from a path, and display each channel as separate images to illustrate color intensities.

Learn to save processed images in OpenCV with Python by reading an image with cv2.imread, rotating it with cv2.rotate, and saving the result with cv2.imwrite as rotated image.jpg.

Learn how to read a video file with OpenCV in Python, resize and save a video, and display frames frame by frame using cv2 with waitKey and release.

Resize a video in Python with OpenCV by reading frames from a video file, applying cv2.resize to halve the width and height, and displaying the resized frames.

Learn how to save a cropped video in Python using OpenCV by capturing frames, cropping to 500 by 500, and writing them to an mp4 file while displaying results.

Draw a circle on an image with OpenCV using cv2.circle, specifying center, radius, color in BGR, and thickness or fill, as part of the read and display workflow.

Learn to add text to images with OpenCV and Python, covering text arguments, font options, color in bgr, scale, thickness, and line type with a practical coding example.

OpenCV with Python shows how to draw a line segment on an image using cv2.line, with two points, BGR color, thickness, and line type, and display with cv2.imshow and waitKey.

Draw a rectangle on an image with OpenCV in Python using cv2.rectangle, specifying opposite corners (top-left and bottom-right or top-right and bottom-left), then adjust color, thickness, line type, or fill.

Learn to draw an ellipse on an image with OpenCV in Python using the ellipse function from cv2, including center, axes, rotation, and start/end angles for complete or partial ellipses.

Learn to apply the not operation on binary images with OpenCV in Python, using bitwise not to invert 0 and 255 values and create image masks.

Learn how cv2.bitwise_and performs a pixel-wise and on images in OpenCV, producing zero when any input pixel is zero and nonzero when both inputs are greater than zero, enabling masking.

Use cv2.bitwise_or on two images of the same shape in python, producing a pixel-wise output where any nonzero input yields 255, enabling masking of relevant regions.

Learn to blend two images in OpenCV with Python using alpha, beta, and gamma, resize to match shapes, and display the blended output.

Learn global thresholding in OpenCV with Python to convert grayscale images into binary images. Apply a threshold value and max value of 255, and explore binary versus binary inverse results.

Learn adaptive thresholding, an improvement over global thresholding, computing region-specific thresholds with mean or weighted-neighborhood methods and varying block sizes.

Discover canny edge detection in OpenCV with Python, featuring noise reduction, gradient computation, non-maximum suppression, and hysteresis thresholding. Tune lower and upper thresholds and aperture size to produce edge-detected output.

Learn image blurring in OpenCV with Python, covering average, Gaussian, and median blur to reduce noise with kernel size and sigma. See input and output comparisons.

Learn to detect and analyze contours in images using OpenCV with Python, converting images to binary, retrieving contour hierarchies, applying chain approximation, and drawing results.

Discover how to compute contour area and perimeter in OpenCV with Python using cv2.findContours, cv2.contourArea, and cv2.arcLength, including orientation and closed parameters for accurate measurements.

Learn to fit shapes to contours using OpenCV with Python by drawing a bounding rectangle and a min enclosing circle on each contour for shape analysis.

Learn to use OpenCV with Python to check if a point is inside, outside, or on a contour using cv2.pointPolygonTest, which returns -1, +1, or 0 and can compute distance.

Explore how the SIFT algorithm detects and describes local image features with 128-length descriptors. Learn how to implement SIFT in OpenCV, identify keypoints, and apply to object recognition and tracking.

Learn feature matching in OpenCV with Python by detecting keypoints and descriptors using the shift algorithm, then matching them with a brute force matcher and drawing the best matches.

Save all frames from a video file using OpenCV in Python by capturing and saving each frame. Create an images directory and write frames as jpg files.

Learn to make a video from images using OpenCV and Python by reading an image folder, resizing frames to a size, and writing with cv2.VideoWriter to mp4 at 15 fps.

Discover how to read images with the pillow library, convert them to OpenCV's BGR format with cv2.cvtColor, and save them without errors.

Congratulate yourself on completing the course and learn how image and video processing works with OpenCV. Read and share your feedback to help other students learn and keep exploring.