我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用cv2.HOGDescriptor()。
def createTrainingInstances(self, images): start = time.time() hog = cv2.HOGDescriptor() instances = [] for img, label in images: # print img img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = hog.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) instances.append(pairing) end = time.time() - start self.training_instances = instances print "HOG TRAIN SERIAL: %d images -> %f" % (len(images), end)
def HogDescriptor(self,image): hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) (rects, weights) = hog.detectMultiScale(image, winStride=(5,5),padding=(16,16), scale=1.05, useMeanshiftGrouping=False) return rects
def hog_compute(ims): samples=[] winSize = (64,64) blockSize = (16,16) blockStride = (8,8) cellSize = (8,8) nbins = 9 derivAperture = 1 winSigma = 4. histogramNormType = 0 L2HysThreshold = 2.0000000000000001e-01 gammaCorrection = 0 nlevels = 64 hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma, histogramNormType,L2HysThreshold,gammaCorrection,nlevels) #compute(img[, winStride[, padding[, locations]]]) -> descriptors winStride = (8,8) padding = (8,8) locations = ((10,20),(30,30),(50,50),(70,70),(90,90),(110,110),(130,130),(150,150),(170,170),(190,190)) for im in ims: hist = hog.compute(im,winStride,padding,locations) samples.append(hist) return np.float32(samples)
def get_hog(image): # winSize = (64,64) winSize = (image.shape[1], image.shape[0]) blockSize = (8,8) # blockSize = (16,16) blockStride = (8,8) cellSize = (8,8) nbins = 9 derivAperture = 1 winSigma = 4. histogramNormType = 0 L2HysThreshold = 2.0000000000000001e-01 gammaCorrection = 0 nlevels = 64 hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma, histogramNormType,L2HysThreshold,gammaCorrection,nlevels) #compute(img[, winStride[, padding[, locations]]]) -> descriptors winStride = (8,8) padding = (8,8) locations = [] # (10, 10)# ((10,20),) hist = hog.compute(image,winStride,padding,locations) return hist
def createTestingInstances(self, images): start = time.time() hog = cv2.HOGDescriptor() instances = [] for img, label in images: # print img img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = hog.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) instances.append(pairing) end = time.time() - start self.testing_instances = instances print "HOG TEST SERIAL: %d images -> %f" % (len(images), end)
def find_people(self, img): ''' Detect people in image :param img: numpy.ndarray :return: count of rectangles after non-maxima suppression, corresponding to number of people detected in picture ''' t = time.time() # HOG descriptor/person detector hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # Chooses whichever size is less image = imutils.resize(img, width=min(self.MIN_IMAGE_WIDTH, img.shape[1])) # detect people in the image (rects, wghts) = hog.detectMultiScale(image, winStride=self.WIN_STRIDE, padding=self.PADDING, scale=self.SCALE) # apply non-maxima suppression to the bounding boxes but use a fairly large overlap threshold, # to try to maintain overlapping boxes that are separate people rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects]) pick = non_max_suppression(rects, probs=None, overlapThresh=self.OVERLAP_THRESHOLD) print("Elapsed time: {} seconds".format(int((time.time() - t) * 100) / 100.0)) if self.SHOW_IMAGES: # draw the final bounding boxes for (xA, yA, xB, yB) in pick: # Tighten the rectangle around each person by a small margin shrinkW, shrinkH = int(0.05 * xB), int(0.15*yB) cv2.rectangle(image, (xA+shrinkW, yA+shrinkH), (xB-shrinkW, yB-shrinkH), self.BOX_COLOR, 2) cv2.imshow("People detection", image) cv2.waitKey(self.IMAGE_WAIT_TIME) cv2.destroyAllWindows() return len(pick)
def __init__(self, scale=1.08): script_path = common.get_script_path() self.cascade = cv2.CascadeClassifier(script_path + "/haarcascade_frontalface_alt.xml") self.cascade_profile = cv2.CascadeClassifier(script_path + '/haarcascade_profileface.xml') self.scale = scale self.hog = cv2.HOGDescriptor() self.hog.load(script_path + '/hard_negative_svm/hog.xml') self.svm = cv2.ml.SVM_load(script_path + '/hard_negative_svm/output_frontal.xml') self.svm_profile = cv2.ml.SVM_load(script_path + '/hard_negative_svm/output_profile.xml')
def compute_hog(image, locations): hog = cv2.HOGDescriptor() winStride = (8, 8) padding = (8, 8) hist = hog.compute(image, winStride, padding, locations) return hist
def get_hog_object(window_dims): blockSize = (8,8) # blockSize = (16,16) blockStride = (8,8) cellSize = (8,8) nbins = 9 derivAperture = 1 winSigma = 4. histogramNormType = 0 # HOGDescriptor::L2Hys L2HysThreshold = 2.0000000000000001e-01 gammaCorrection = 0 nlevels = 64 hog = cv2.HOGDescriptor(window_dims,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma, histogramNormType,L2HysThreshold,gammaCorrection,nlevels) return hog
def create_hog_from_info_dict(info): print info hog = cv2.HOGDescriptor( tuple(info['winSize']), tuple(info['blockSize']), tuple(info['blockStride']), tuple(info['cellSize']), info['nbins'], info['derivAperture'], info['winSigma'], info['histogramNormType'], info['L2HysThreshold'], info['gammaCorrection'], info['nlevels']) return hog
def hog_features(image): hog_computer = cv2.HOGDescriptor() return hog_computer.compute(image)
def createTrainingInstances(self, images): hog = cv2.HOGDescriptor() instances = [] for img, label in images: print img img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = hog.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) instances.append(pairing) self.training_instances = instances
def createTestingInstances(self, images): hog = cv2.HOGDescriptor() instances = [] for img, label in images: print img img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = hog.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) instances.append(pairing) self.testing_instances = instances
def local_hog(image): HOGDESC = cv2.HOGDescriptor() img, label = image img = read_color_image(img) img = cv2.resize(img, (128, 128), interpolation = cv2.INTER_AREA) descriptor = HOGDESC.compute(img) if descriptor is None: descriptor = [] else: descriptor = descriptor.ravel() pairing = Instance(descriptor, label) return pairing
def find_people(self, img): ''' Detect people in image :param img: numpy.ndarray :return: count of rectangles after non-maxima suppression, corresponding to number of people detected in picture ''' t = time.time() hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # Chooses whichever size is less image = imutils.resize(img, width=min(self.MIN_IMAGE_WIDTH, img.shape[1])) # detect people in the image (rects, wghts) = hog.detectMultiScale(image, winStride=self.WIN_STRIDE, padding=self.PADDING, scale=self.SCALE) # apply non-maxima suppression to the bounding boxes using a # fairly large overlap threshold to try to maintain overlapping boxes that are still people rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects]) pick = non_max_suppression(rects, probs=None, overlapThresh=self.OVERLAP_THRESHOLD) print("Elapsed time of detection: {} seconds".format(int((time.time() - t) * 100) / 100.0)) if self.SHOW_IMAGES: # draw the final bounding boxes for (xA, yA, xB, yB) in pick: # Tighten the rectangle around each person by a small margin cv2.rectangle(image, (xA+5, yA+5), (xB-5, yB-10), self.BOX_COLOR, 2) cv2.imshow("People detection", image) cv2.waitKey(self.IMAGE_WAIT_TIME) cv2.destroyAllWindows() return len(pick)
def _extract_feature(X, feature): """Performs feature extraction :param X: data (rows=images, cols=pixels) :param feature: which feature to extract - None: no feature is extracted - "gray": grayscale features - "rgb": RGB features - "hsv": HSV features - "surf": SURF features - "hog": HOG features :returns: X (rows=samples, cols=features) """ # transform color space if feature == 'gray' or feature == 'surf': X = [cv2.cvtColor(x, cv2.COLOR_BGR2GRAY) for x in X] elif feature == 'hsv': X = [cv2.cvtColor(x, cv2.COLOR_BGR2HSV) for x in X] # operate on smaller image small_size = (32, 32) X = [cv2.resize(x, small_size) for x in X] # extract features if feature == 'surf': surf = cv2.SURF(400) surf.upright = True surf.extended = True num_surf_features = 36 # create dense grid of keypoints dense = cv2.FeatureDetector_create("Dense") kp = dense.detect(np.zeros(small_size).astype(np.uint8)) # compute keypoints and descriptors kp_des = [surf.compute(x, kp) for x in X] # the second element is descriptor: choose first num_surf_features # elements X = [d[1][:num_surf_features, :] for d in kp_des] elif feature == 'hog': # histogram of gradients block_size = (small_size[0] / 2, small_size[1] / 2) block_stride = (small_size[0] / 4, small_size[1] / 4) cell_size = block_stride num_bins = 9 hog = cv2.HOGDescriptor(small_size, block_size, block_stride, cell_size, num_bins) X = [hog.compute(x) for x in X] elif feature is not None: # normalize all intensities to be between 0 and 1 X = np.array(X).astype(np.float32) / 255 # subtract mean X = [x - np.mean(x) for x in X] X = [x.flatten() for x in X] return X
def detect(): move=0 hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) cap=cv2.VideoCapture(0) while(1): ret, img=cap.read() gray=cv2. cvtColor(img, cv2.COLOR_BGR2GRAY) image = imutils.resize(img, width=min(400, img.shape[1])) (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),padding=(8, 8), scale=1.05) for (x, y, w, h) in rects: cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2) rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects]) pick = non_max_suppression(rects, probs=None, overlapThresh=0.65) for (xA, yA, xB, yB) in pick: cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2) if (xA/480)>0.5 : print("move to right") move=4 elif (yA/640)>0.5: print('move to down') move=3 elif (xB/480)<0.3: print('move to left') move=2 elif (yB/640)<0.3: print('move to up') move=1 else: print('do nothing') move=0 mqt.pass_message(move) #eyes = eye_cascade.detectMultiScale(roi_gray) #for (ex,ey,ew,eh) in eyes: # cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2) cv2.imshow('img',image) k=cv2.waitKey(1)& 0xff if k==27: break elif (k==ord('w')): mqt.pass_message(1) elif (k==ord('s')): mqt.pass_message(3) cap.release() cv2.destroyAllWindows()
