Image color and shape recognition based on OpenCV is achieved through the following principles:
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Image preprocessing: First, convert the color image into a grayscale image. This is done because in grayscale images, each pixel has only one color channel, which makes subsequent processing easier.
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Threshold segmentation: perform threshold segmentation on grayscale images and convert the images into binary images. Threshold segmentation sets a threshold, and sets the pixels in the image with a grayscale value higher than the threshold as white (255), and the pixels with a grayscale value below the threshold as black (0). This separates the target object in the image from the background.
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Contour detection: Use OpenCV's contour detection function
cv2.findContours()to detect contours in binary images. A contour is a series of connected boundary points that can represent the shape of a target object. -
Shape feature extraction : Determine the shape of the target object by calculating the perimeter, area and other features of the outline . For example, you can calculate the perimeter of a contour and then
cv2.approxPolyDP( )approximately fit the contour using the approximate polygon method to obtain the number of vertices of the polygon. Based on the number of vertices and other features, shapes such as square, rectangle, rhombus, etc. can be determined. -
Color recognition: In the pre-processing stage, the image can be color segmented or filtered according to needs to extract the color area of interest. For example, you can use
cv2.inRange()the function to set the color range, set the color within the range to white, and set the color outside the range to black, thereby extracting the color area of the target object.
Based on the above steps, image color and shape recognition can be achieved through OpenCV. The specific implementation methods vary according to specific needs and the complexity of the scenario, and may need to be improved and optimized in combination with other algorithms and technologies.