Learn Python OpenCV Easily: Drawing Basic Geometric Shapes
This article introduces methods to draw basic geometric shapes using OpenCV. The steps are as follows: First, install the opencv-python and numpy libraries. After importing these libraries, create a 500x500 black canvas. For drawing shapes: Lines are drawn using cv2.line, e.g., an anti-aliased red line from (50,50) to (450,450); Rectangles are drawn using cv2.rectangle, supporting both outlines (line width 3) and fill (line width -1), such as a green outlined rectangle and a blue filled rectangle; Circles are drawn using cv2.circle, supporting both outlines (line width 5) and fill (line width -1), such as a yellow outlined circle and a red filled circle; Polygons are drawn using cv2.polylines (for outlines) and cv2.fillPoly (for filling), with an example being a cyan triangular outline and a light red quadrilateral fill. Finally, display the image with cv2.imshow and wait for user input to close using cv2.waitKey. Key notes: Colors are in BGR format (e.g., red is (0,0,255)), line width -1 indicates filling, and the coordinate origin is at the top-left corner of the image.
Read MorePython OpenCV Practical: Template Matching and Image Localization
This paper introduces an image localization method using Python OpenCV to implement template matching. The core of template matching is sliding a "template image" over a target image and calculating similarity to find the most matching region, which is suitable for simple scenarios (e.g., monitoring object localization). The steps include: preparing target and template images, converting them to grayscale to improve efficiency; using `matchTemplate` (e.g., the `TM_CCOEFF_NORMED` method) to calculate the similarity matrix; setting a threshold (e.g., 0.8) to filter high-similarity regions and using `np.where` to obtain their positions; finally, marking the matching results with rectangles and displaying/saving them. Note: Template matching is only applicable to scenarios where the target has no rotation or scaling; for complex scenarios, feature matching like ORB should be used instead. The matching method and threshold need to be adjusted according to actual conditions—too high a threshold may lead to missed detections, while too low may cause false positives. Through the practical example of "apple localization," this paper helps beginners master the basic process, making it suitable for quickly implementing simple image localization tasks.
Read MoreLearning Python OpenCV from Scratch: Real - time Capture and Display with Camera
This article introduces a method to achieve real - time camera capture and display using Python and OpenCV. The reasons for choosing OpenCV (Open Source Computer Vision Library) and Python (with concise syntax) are their ease of use and functional adaptability. The opencv - python interface for Python is easy to install. Installation steps: First, install Python 3.6 or higher, and then install the library through `pip install opencv - python` (numpy may need to be installed first if necessary). Core process: Open the camera (`cv2.VideoCapture(0)`), loop to read frames (`cap.read()`, which returns ret and frame), display the image (`cv2.imshow()`), press the 'q' key to exit, and release resources (`cap.release()` and `cv2.destroyAllWindows()`). Key code explanation: `cap.read()` checks the reading status, `cv2.waitKey(1)` waits for a key press (the 'q' key to exit), and ensures that resources are correctly released to avoid occupation. The article also mentions common problems (such as the camera not opening) and extended exercises (such as grayscale display, image flipping, etc.), laying a foundation for subsequent complex image processing.
Read MorePython OpenCV Image Scaling and Cropping: Essential Techniques for Beginners
This article introduces basic operations of image resizing and cropping in Python OpenCV, helping beginners master core techniques. **Image Resizing**: Use the `cv2.resize()` function, supporting two target size specification methods: scaling by ratio (controlled via `fx`/`fy`, e.g., `fx=0.5` to halve the size) or directly specifying width and height (e.g., `(200, 200)`). Recommended interpolation methods: `INTER_AREA` for shrinking and `INTER_LINEAR` for enlarging to avoid distortion. In examples, pay attention to correct image path and window operations (`waitKey` and `destroyAllWindows`). **Image Cropping**: Essentially involves NumPy array slicing with the format `img[y_start:y_end, x_start:x_end]`, ensuring coordinates do not exceed bounds (`y_end` ≤ height, `x_end` ≤ width). Examples include fixed-region cropping and center-region cropping (calculating center offsets `(w-target_w)//2` and `(h-target_h)//2` before slicing). **Summary**: Resizing requires attention to path and interpolation methods, while cropping must focus on coordinate ranges. These two operations are often used together (e.g., cropping first then resizing) and are fundamental in image preprocessing.
Read MoreStep-by-Step Guide to Image Contour Detection with Python OpenCV
This article introduces a method for image contour recognition using Python OpenCV. First, the OpenCV and NumPy libraries need to be installed. Image contours are the boundary lines of objects, used to locate target objects (such as faces, circles). The core steps include: preprocessing (grayscale conversion + binarization to simplify the image), edge detection (Canny algorithm to determine boundaries through thresholds), contour extraction (obtaining coordinates via findContours), and filtering and drawing (filtering by area and other criteria and visualizing). In practice, taking "shapes.jpg" as an example, the process is demonstrated: reading the image → grayscale conversion + binarization → Canny edge detection → findContours to extract contours → filtering the largest contour by area and drawing it. Common issues like incomplete contours can be addressed by adjusting Canny thresholds, and excess contours can be resolved through area filtering. It can also be extended to recognize objects using shape features such as circularity. In summary, contour recognition is a foundation in computer vision. Beginners can start with simple images and optimize results through parameter adjustments.
Read MoreFrom Beginner to Practical: A Detailed Explanation of Python OpenCV Color Space Conversion
This article introduces the concept of image color spaces and the conversion applications in Python using OpenCV. Common color spaces include RGB (for display, with red/green/blue channels), BGR (OpenCV default, in blue/green/red order), and HSV (hue H, saturation S, value V, suitable for color segmentation). The conversion reasons are that different spaces serve different purposes (RGB for display, HSV for color recognition, BGR as OpenCV's native format). The core tool is `cv2.cvtColor()`, with the syntax `cv2.cvtColor(img, cv2.COLOR_originalSpace2targetSpace)`, e.g., `cv2.COLOR_BGR2HSV`. In practice, taking red object detection as an example: read the image → convert to HSV → define the red HSV range (H values in 0-10 and 160-179 intervals) → extract via mask. It can also be extended to real-time detection with a camera. Key points: master the conversion function, note the difference between BGR and RGB, and adjust HSV ranges according to light conditions.
Read MorePython OpenCV Tutorial: Master Image Binarization in 5 Minutes
Image binarization is a process that classifies pixels into black and white categories based on a threshold, simplifying images for easier analysis, and is commonly used in scenarios such as text recognition. The core implementation relies on the `cv2.threshold()` function, which requires inputting a grayscale image, a threshold value, a maximum value, and a type, returning the actual threshold and the binarized image. Common threshold types include: `THRESH_BINARY` (pixels above the threshold turn white), `THRESH_BINARY_INV` (the opposite), and `THRESH_OTSU` (automatically calculates the optimal threshold). For threshold selection: manual selection is suitable for images with uniform brightness, Otsu's method is ideal for high-contrast scenarios, and adaptive thresholds are used for uneven lighting. The key steps are: reading the image and converting it to grayscale → selecting the threshold type → performing binarization → displaying the result. Mastering binarization supports tasks such as edge detection and object segmentation.
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