Find opencv-python API for image contour: findContours function
This function takes as parameters binary map, according to the parameter, the outer contour of the object to find, and external profile, save profile point, compression etc ...如：contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
def findContours(image, mode, method, contours=None, hierarchy=None, offset=None): # real signature unknown; restored from __doc__
"""
findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchy
. @brief Finds contours in a binary image.
.
. The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
. are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
. OpenCV sample directory.
. @note Since opencv 3.2 source image is not modified by this function.
.
. @param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
. pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
. #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
. If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
. @param contours Detected contours. Each contour is stored as a vector of points (e.g.
. std::vector<std::vector<cv::Point> >).
. @param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
. as many elements as the number of contours. For each i-th contour contours[i], the elements
. hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
. in contours of the next and previous contours at the same hierarchical level, the first child
. contour and the parent contour, respectively. If for the contour i there are no next, previous,
. parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
. @param mode Contour retrieval mode, see #RetrievalModes
. @param method Contour approximation method, see #ContourApproximationModes
. @param offset Optional offset by which every contour point is shifted. This is useful if the
. contours are extracted from the image ROI and then they should be analyzed in the whole image
. context.
"""
pass| parameter | effect |
|---|---|
| cv2.RETR_EXTERNAL | Find only the outer contour |
| cv2.RETR_LIST | All contour detection, save it to a arry (list) |
| cv2.RETR_CCOMP | Establish two levels contour (outer / inner), only two layers of tissue |
| cv2.RETR_TREE | All contour detection, reconstruction of all levels of nested profile |
| cv2.CHAIN_APPROX_NONE | Store all border points |
| cv2.CHAIN_APPROX_SIMPLE | Compressed vertically, horizontally, diagonally, leaving only the endpoint |
| cv2.CHAIN_APPROX_TX89_L1 | Approximate algorithm using teh-Chini |
| cv2.CHAIN_APPROX_TC89_KCOS | Approximate algorithm using teh-Chini |
cv2.drawContours (image, contours, contourIdx, color [, thickness [, lineType [, hierarchy [, maxLevel [, offset]]]]])
The first argument is specified on the drawn contour which image;
second parameter It contour itself, in Python is a list.
The third parameter specifies which of contouring strip profile list, if it is -1, which draw all contour. The latter parameter is simple. Wherein the thickness indicates that the width of the contour line, if it is -1 (cv2.FILLED), was fill mode
Find profile
contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours multidimensional array comprising a plurality of contours cnt
x, y, w, h = cv2.boudingrect (cnt) # circumscribed rectangle obtained
Parameters: x, y, w, h respectively represent an x-axis coordinate of the circumscribed rectangle and the y-axis, and the width and height of the rectangle, cnt represents the profile value input