python类randn()的实例源码

def augment_image(rgbImg):
    augmented_images = []

    # original image
    augmented_images.append(rgbImg)

    # fliped x-axis
    rimg = rgbImg.copy()
    cv2.flip(rimg, 1, rimg)
    augmented_images.append(rimg)

    # add gaussian noise
    for _ in range(10):
        gaussian_noise = rgbImg.copy()
        cv2.randn(gaussian_noise, 0, 150)
        augmented_images.append(rgbImg + gaussian_noise)
        augmented_images.append(rimg + gaussian_noise)

    for _ in range(10):
        uniform_noise = rgbImg.copy()
        cv2.randu(uniform_noise, 0, 1)
        augmented_images.append(rgbImg + uniform_noise)
        augmented_images.append(rimg + uniform_noise)

    return augmented_images

def read(self, dst=None):
        w, h = self.frame_size

        if self.bg is None:
            buf = np.zeros((h, w, 3), np.uint8)
        else:
            buf = self.bg.copy()

        self.render(buf)

        if self.noise > 0.0:
            noise = np.zeros((h, w, 3), np.int8)
            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
            buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
        return True, buf

def read(self, dst=None):
        w, h = self.frame_size

        if self.bg is None:
            buf = np.zeros((h, w, 3), np.uint8)
        else:
            buf = self.bg.copy()

        self.render(buf)

        if self.noise > 0.0:
            noise = np.zeros((h, w, 3), np.int8)
            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
            buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
        return True, buf

def read(self, dst=None):
        w, h = self.frame_size

        if self.bg is None:
            buf = np.zeros((h, w, 3), np.uint8)
        else:
            buf = self.bg.copy()

        self.render(buf)

        if self.noise > 0.0:
            noise = np.zeros((h, w, 3), np.int8)
            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
            buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
        return True, buf

def applyNoise(self, gaussian):

        # clone HSV channels first
        from copy import deepcopy
        self.n_h = deepcopy(self.h)
        self.n_s = deepcopy(self.s)
        self.n_v = deepcopy(self.v)

        mean, var = gaussian.get('mean'), gaussian.get('var')

        cv2.randn(self.n_h, mean, var)
        cv2.randn(self.n_s, mean, var)
        cv2.randn(self.n_v, mean, var)

def read(self, dst=None):
        w, h = self.frame_size

        if self.bg is None:
            buf = np.zeros((h, w, 3), np.uint8)
        else:
            buf = self.bg.copy()

        self.render(buf)

        if self.noise > 0.0:
            noise = np.zeros((h, w, 3), np.int8)
            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
            buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
        return True, buf

def read(self, dst=None):
        noise = np.zeros(self.render.sceneBg.shape, np.int8)
        cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)

        return True, cv2.add(self.render.getNextFrame(), noise, dtype=cv2.CV_8UC3)

def read(self, dst=None):
        noise = np.zeros(self.render.sceneBg.shape, np.int8)
        cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)

        return True, cv2.add(self.render.getNextFrame(), noise, dtype=cv2.CV_8UC3)

def read(self, dst=None):
        w, h = self.frame_size

        if self.bg is None:
            buf = np.zeros((h, w, 3), np.uint8)
        else:
            buf = self.bg.copy()

        self.render(buf)

        if self.noise > 0.0:
            noise = np.zeros((h, w, 3), np.int8)
            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
            buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
        return True, buf

def read(self, dst=None):
        w, h = self.frame_size

        if self.bg is None:
            buf = np.zeros((h, w, 3), np.uint8)
        else:
            buf = self.bg.copy()

        self.render(buf)

        if self.noise > 0.0:
            noise = np.zeros((h, w, 3), np.int8)
            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
            buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
        return True, buf

def read(self, dst=None):
        noise = np.zeros(self.render.sceneBg.shape, np.int8)
        cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)

        return True, cv2.add(self.render.getNextFrame(), noise, dtype=cv2.CV_8UC3)

def read(self, dst=None):
        noise = np.zeros(self.render.sceneBg.shape, np.int8)
        cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)

        return True, cv2.add(self.render.getNextFrame(), noise, dtype=cv2.CV_8UC3)