def cal_center_region_hist(self, image): # initialize the color descriptor # using 8 Hue bins, 12 Saturation bins, and 3 Value bins cd = ColorDescriptor((8, 12, 3)) # convert the image to the HSV color space and initialize # the features used to quantify the image image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) features = [] # grab the dimensions and compute the center of the image (h, w) = image.shape[:2] (cX, cY) = (int(w * 0.5), int(h * 0.5)) # construct an elliptical mask representing the center of the image (axesX, axesY) = (int(w * 0.75) / 2, int(h * 0.75) / 2) ellipMask = np.zeros(image.shape[:2], dtype = "uint8") cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1) # extract a color histogram from the elliptical region and # update the feature vector hist = cd.histogram(image, ellipMask) hist = self.kmeans_region_features(hist) features.extend(hist.flatten()) # return the feature vector return features
def cal_region_hist(self, image): # initialize the color descriptor # using 8 Hue bins, 12 Saturation bins, and 3 Value bins cd = ColorDescriptor((8, 12, 3)) # convert the image to the HSV color space and initialize # the features used to quantify the image image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) features = [] # grab the dimensions and compute the center of the image (h, w) = image.shape[:2] (cX, cY) = (int(w * 0.5), int(h * 0.5)) # divide the image into four rectangles/segments (top-left, # top-right, bottom-right, bottom-left) segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h), (0, cX, cY, h)] # construct an elliptical mask representing the center of the # image (axesX, axesY) = (int(w * 0.75) / 2, int(h * 0.75) / 2) ellipMask = np.zeros(image.shape[:2], dtype = "uint8") cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1) # loop over the segments for (startX, endX, startY, endY) in segments: # construct a mask for each corner of the image, subtracting # the elliptical center from it cornerMask = np.zeros(image.shape[:2], dtype = "uint8") cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1) cornerMask = cv2.subtract(cornerMask, ellipMask) # extract a color histogram from the image, then update the # feature vector hist = cd.histogram(image, cornerMask) hist = self.kmeans_region_features(hist) features.extend(hist.flatten()) # extract a color histogram from the elliptical region and # update the feature vector hist = cd.histogram(image, ellipMask) hist = self.kmeans_region_features(hist) features.extend(hist.flatten()) # return the feature vector return features
