star_detection.py 文件源码

def detect(self, image):
        floatimage = cv2.cvtColor(np.float32(image), cv2.COLOR_BGR2GRAY) / 255

        if self.gaussian is None or self.gaussian.shape[0] != Configuration.log_kernel_size:
            self.gaussian = cv2.getGaussianKernel(Configuration.log_kernel_size, -1, cv2.CV_32F)

        gaussian_filtered = cv2.sepFilter2D(floatimage, cv2.CV_32F, self.gaussian, self.gaussian)

        # LoG
        filtered = cv2.Laplacian(gaussian_filtered, cv2.CV_32F, ksize=Configuration.log_block_size)

        # DoG
        #gaussian2 = cv2.getGaussianKernel(Configuration.log_block_size, -1, cv2.CV_32F)
        #gaussian_filtered2 = cv2.sepFilter2D(floatimage, cv2.CV_32F, gaussian2, gaussian2)
        #filtered = gaussian_filtered - gaussian_filtered2

        mi = np.min(filtered)
        ma = np.max(filtered)

        if mi - ma != 0:
            filtered = 1 - (filtered - mi) / (ma - mi)

        _, thresholded = cv2.threshold(filtered, Configuration.log_threshold, 1.0, cv2.THRESH_BINARY)
        self.debug = thresholded
        thresholded = np.uint8(thresholded)

        contours = None

        if int(cv2.__version__.split('.')[0]) == 2:
            contours, _ = cv2.findContours(thresholded, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        else:
            _, contours, _ = cv2.findContours(thresholded, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

        candidates = []

        for i in range(len(contours)):
            rect = cv2.boundingRect(contours[i])
            v1 = rect[0:2]
            v2 = np.add(rect[0:2], rect[2:4])
            if rect[2] < Configuration.log_max_rect_size and rect[3] < Configuration.log_max_rect_size:
                roi = floatimage[v1[1]:v2[1], v1[0]:v2[0]]
                _, _, _, maxLoc = cv2.minMaxLoc(roi)
                maxLoc = np.add(maxLoc, v1)

                candidates.append(maxLoc)

        self.candidates = candidates

        return candidates