You can vote up the examples you like or vote down the ones you don't like. image为输入的灰度图像2. # A bigger scale_factor means bigger jumps so it will perform # faster but less accuratelly, it is recommended 1.2 or 1.3 scale_factor = 1.2 # Treshold to detect a face, it needs a minimum of min_neighbors # neighbor pixels to return a detected a face on that pixel min_neighbors = 5 # Sets the min_size of the face we want to detect. Method Detail public void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flag, Size minSize, Size maxSize) image: CV_8U型の行列。ここに格納されていいる画像中から 物体が検出されます。 objects: 矩形を要素とするベクトル。 Expected behaviour When using detectMultiScale I expect python to do a clean exit. UnityでOpenCVを使うには. faces = faceCascade.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=5, minSize=(20, 20) ) UnityでOpenCVの使い方を調べるとまず一番多く出てくるのはきっと「OpenCV For Unity」を使う方法だと思いますが、お金がかかってしまうので今回は別の方法でやります。 objects为得到被检测物体的矩形框向量组3. ourClassifier.detectMultiScale(input image, Scale Factor, Min Neighbors) Scale Factor Specifies how much we reduce the image size each time we scale… Documentation: Python: cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]])→ objects Detects objects of different sizes in the input image. minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it. cvHaarDetectObjects是opencv1中的函数，opencv2中人脸检测使用的是 detectMultiScale函数。它可以检测出图片中所有的人脸，并将人脸用vector保存各个人脸的坐标、大小（用矩形表示），函数由分类器对象调用：
The image is repeatedly scaled down by the specified Scale factor, as long as it remains larger than hog.WinSize or until a maximum of hog.NLevels levels is built. Now we must call our classifier function, passing it some very important parameters, as scale factor, number of neighbors and minimum size of the detected face.
The detected objects are returned as a list of rectangles The following are code examples for showing how to use cv2.CASCADE_SCALE_IMAGE().They are from open source Python projects. The parameters defined inside detectMultiScale other than the input image have the following significance. detectMultiScale（const Mat& image, vector& objects, double scaleFactor=1.1，int minNeighbors, int flag，cvSize）1.
The resized images are then searched with a sliding window to detect objects similar to the cv.HOGDescriptor.detect method (this method is parallelized). cv.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) objects, numDetections = ... Parameter specifying how much the image size is reduced at each image scale. CascadeClassifier检测的基本原理：xml中存放的是训练后的特征池，特征size大小根据训练时的参数而定，检测的时候可以简单理解为就是将每个固定size特征（检测窗口）与输入图像的同样大小区域比较，如果匹配那么就记录这个矩形区域的位置，然后滑动窗口，检测图像的另一个区域，重复操作。 scaleFactor为每一个图像尺度中的尺度参数，默认值python 二、detectMultiScale函数详解 .