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Contours are curves that join all the continuous points along the boundary of an object with the same color or intensity. They are useful for shape analysis, object detection, and recognition. OpenCV provides functions to find and draw contours in images.
Finding Contours
To find contours in an image, follow these steps:
Convert the Image to Grayscale: This simplifies the image and prepares it for thresholding.
Apply Binary Thresholding: This converts the image to a binary format, highlighting the objects of interest.
Find Contours: Use the cv2.findContours() function to detect contours in the binary image.
Here is an example of finding contours in an image:
import cv2import numpy as np# Read the imageimage = cv2.imread('test.jpg')assert image is not None, "file could not be read, check with os.path.exists()"# Convert the image to grayscalegray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# Apply binary thresholdingret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)# Find contourscontours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) Contour Detection using OpenCV (Python/C++)
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How Contour Detection Works. At a high level, here is the 5-step process for contour detection in OpenCV: Read a color image; Convert the image to grayscale; Convert the image to binary (i.e. black and white only) using Otsu’s …
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