当前位置: 首页>>代码示例>>Python>>正文


Python FeatureExtractor.compute_descriptors方法代码示例

本文整理汇总了Python中feature_extractor.FeatureExtractor.compute_descriptors方法的典型用法代码示例。如果您正苦于以下问题:Python FeatureExtractor.compute_descriptors方法的具体用法?Python FeatureExtractor.compute_descriptors怎么用?Python FeatureExtractor.compute_descriptors使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在feature_extractor.FeatureExtractor的用法示例。


在下文中一共展示了FeatureExtractor.compute_descriptors方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: Tester

# 需要导入模块: from feature_extractor import FeatureExtractor [as 别名]
# 或者: from feature_extractor.FeatureExtractor import compute_descriptors [as 别名]
class Tester(object):
    def __init__(self, numGestures, minDescriptorsPerFrame, numWords, descType, numPredictions, parent):
        self.numGestures = numGestures
        self.numWords = numWords
        self.minDescriptorsPerFrame = minDescriptorsPerFrame
        self.parent = parent
        self.classifier = None
        self.windowName = "Testing preview"
        self.handWindowName = "Cropped hand"
        self.binaryWindowName = "Binary frames"
        self.predictionList = [-1]*numPredictions;
        self.handTracker = HandTracker(kernelSize=7, thresholdAngle=0.4, defectDistFromHull=30, parent=self)
        self.featureExtractor = FeatureExtractor(type=descType, parent=self)
        self.numSideFrames = 10
        self.prevFrameList = np.zeros((self.numSideFrames,self.parent.imHeight/self.numSideFrames,self.parent.imWidth/self.numSideFrames,3), "uint8")
        self.numPrevFrames = 0
        self.predictionScoreThreshold = 0.2
        self.learningRate = 0.01
        self.numReinforce = 1

    def initialize(self, clf):
        self.classifier = clf
        self.numWords = self.classifier.voc.shape[0]
        self.prevStates = np.zeros((self.numSideFrames, self.numWords), "float32")
        self.prevLabels = [0]*self.numSideFrames
        self.prevScores = [0]*self.numSideFrames

    def test_on_video(self):
        vc = self.parent.vc
        while(vc.isOpened()):
            ret,im = vc.read()
            im = cv2.flip(im, 1)
            imhsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
            self.handTracker.colorProfiler.draw_color_windows(im, imhsv)
            cv2.imshow(self.windowName, im)
            k = cv2.waitKey(1)
            if k == 32: # space
                break
            elif k == 27:
                sys.exit(0)

        self.handTracker.colorProfiler.run()
        binaryIm = self.handTracker.get_binary_image(imhsv)
        cnt,hull,centroid,defects = self.handTracker.initialize_contour(binaryIm)
        cv2.namedWindow(self.binaryWindowName)
        cv2.namedWindow(self.handWindowName)
        cv2.namedWindow(self.windowName)
        cv2.setMouseCallback(self.windowName, self.reinforce)

        while(vc.isOpened()):
            ret,im = vc.read()
            im = cv2.flip(im, 1)
            imhsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
            imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
            binaryIm = self.handTracker.get_binary_image(imhsv)
            cnt,hull,centroid,defects = self.handTracker.get_contour(binaryIm)
            imCopy = 1*im
            testData = None
            prediction = -1
            score = -1
            update = False
            if cnt is not None:
                numDefects = defects.shape[0]
                cropImage,cropPoints = self.handTracker.get_cropped_image_from_cnt(im, cnt, 0.05)
                cropImageGray = self.handTracker.get_cropped_image_from_points(imgray, cropPoints)
                #cv2.fillPoly(binaryIm, cnt, 255)
                #cropImageBinary = self.handTracker.get_cropped_image_from_points(binaryIm, cropPoints)
                #cropImageGray = self.apply_binary_mask(cropImageGray, cropImageBinary, 5)
                #kp,des = self.featureExtractor.get_keypoints_and_descriptors(cropImageGray)
                kp = self.featureExtractor.get_keypoints(cropImageGray)
                cropCnt = self.handTracker.get_cropped_contour(cnt, cropPoints)
                kp = self.featureExtractor.get_keypoints_in_contour(kp, cropCnt)
                kp,des = self.featureExtractor.compute_descriptors(cropImageGray, kp)
                if des is not None and des.shape[0] >= 0:
                    self.featureExtractor.draw_keypoints(cropImage, kp)
                if des is not None and des.shape[0] >= self.minDescriptorsPerFrame and self.is_hand(defects):
                    words, distance = vq(des, self.classifier.voc)
                    testData = np.zeros(self.numWords, "float32")
                    for w in words:
                        testData[w] += 1
                    normTestData = np.linalg.norm(testData, ord=2) * np.ones(self.numWords)
                    testData = np.divide(testData, normTestData)
                    prediction,score = self.predict(testData)
                    sortedScores = np.sort(score)
                    #if max(score) > self.predictionScoreThreshold:
                    if sortedScores[-1]-sortedScores[-2] >= self.predictionScoreThreshold:
                        self.handTracker.draw_on_image(imCopy, cnt=False, hullColor=(0,255,0))
                    else:
                        self.handTracker.draw_on_image(imCopy, cnt=False, hullColor=(255,0,0))
                        prediction = -1
                    update = True
                else:
                    self.handTracker.draw_on_image(imCopy, cnt=False, hullColor=(0,0,255))
                    prediction = -1
                cv2.imshow(self.handWindowName,cropImage)
            else:
                prediction = -1
            #self.insert_to_prediction_list(prediction)
            #prediction,predictionCount = self.most_common(self.predictionList)
            #if prediction>=0:
#.........这里部分代码省略.........
开发者ID:arpitgit,项目名称:Talk2dHand,代码行数:103,代码来源:tester.py


注:本文中的feature_extractor.FeatureExtractor.compute_descriptors方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。