Python cv2.COLOR_BGR2HLS属性代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def color_aug(img, random_h=36, random_l=50, random_s=50):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2HLS).astype(float)

    h = (random.random() * 2 - 1.0) * random_h
    l = (random.random() * 2 - 1.0) * random_l
    s = (random.random() * 2 - 1.0) * random_s

    img[..., 0] += h
    img[..., 0] = np.minimum(img[..., 0], 180)

    img[..., 1] += l
    img[..., 1] = np.minimum(img[..., 1], 255)

    img[..., 2] += s
    img[..., 2] = np.minimum(img[..., 2], 255)

    img = np.maximum(img, 0)
    img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_HLS2BGR)
    return img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def find_edges(img, s_thresh=s_thresh, sx_thresh=sx_thresh, dir_thresh=dir_thresh):

    img = np.copy(img)
    # Convert to HSV color space and threshold the s channel
    hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS).astype(np.float)
    s_channel = hls[:,:,2]
    s_binary = threshold_col_channel(s_channel, thresh=s_thresh)

    # Sobel x
    sxbinary = abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=sx_thresh)
    # mag_binary = mag_thresh(img, sobel_kernel=3, thresh=m_thresh)
    # # gradient direction
    dir_binary = dir_threshold(img, sobel_kernel=3, thresh=dir_thresh)
    #
    # # output mask
    combined_binary = np.zeros_like(s_channel)
    combined_binary[(( (sxbinary == 1) & (dir_binary==1) ) | ( (s_binary == 1) & (dir_binary==1) ))] = 1

    # add more weights for the s channel
    c_bi = np.zeros_like(s_channel)
    c_bi[( (sxbinary == 1) & (s_binary==1) )] = 2

    ave_binary = (combined_binary + c_bi)

    return ave_binary 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def change_saturation(self):
        if self.raw_image is None:
            return 0

        value = self.ui.horizontalSlider.value()
        img_hsv = cv2.cvtColor(self.current_img, cv2.COLOR_BGR2HLS)
        if value > 2:
            img_hsv[:, :, 2] = np.log(img_hsv[:, :, 2] /255* (value - 1)+1) / np.log(value + 1) * 255
        if value < 0:
            img_hsv[:, :, 2] = np.uint8(img_hsv[:, :, 2] / np.log(- value + np.e))
        self.current_img = cv2.cvtColor(img_hsv, cv2.COLOR_HLS2BGR)

# 明度调节 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def test_from_colorspace(self):
        # test BGR colorspace
        aug = iaa.FastSnowyLandscape(
            lightness_threshold=100,
            lightness_multiplier=2.0,
            from_colorspace="BGR")
        image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
        image_hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
        mask = (image_hls[..., 1] < 100)
        expected = np.copy(image_hls).astype(np.float32)
        expected[..., 1][mask] *= 2.0
        expected = np.clip(np.round(expected), 0, 255).astype(np.uint8)
        expected = cv2.cvtColor(expected, cv2.COLOR_HLS2BGR)
        observed = aug.augment_image(image)
        assert np.array_equal(observed, expected) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def test_detect():
    dev = AndroidDeviceMinicap()
    dev._adb.start_minitouch()
    time.sleep(3)

    d = SceneDetector('txxscene')
    old, new = None, None
    while True:
        # time.sleep(0.3)
        screen = dev.screenshot_cv2()
        h, w = screen.shape[:2]
        img = cv2.resize(screen, (w/2, h/2))

        # find hsv
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
        _, _, V = cv2.split(hsv)
        V[V<150] = 0
        cv2.imshow('V', V)
        _, _, L = cv2.split(hls)
        L[L<150] = 0
        cv2.imshow('H', L)

        tic = time.clock()
        new = str(d.detect(img))
        t = time.clock() - tic
        if new != old:
            print 'change to', new
            print 'cost time', t
        old = new

        for _, r in d.current_scene:
            x, y, x1, y1 = r
            cv2.rectangle(img, (x,y), (x1,y1), (0,255,0) ,2)
        cv2.imshow('test', img)
        cv2.waitKey(1) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def change_darker(self):
        if self.raw_image is None:
            return 0
        value = self.ui.horizontalSlider_4.value()
        img_hsv = cv2.cvtColor(self.current_img, cv2.COLOR_BGR2HLS)
        if value > 3:
            img_hsv[:, :, 1] = np.log(img_hsv[:, :, 1] /255* (value - 1)+1) / np.log(value + 1) * 255
        if value < 0:
            img_hsv[:, :, 1] = np.uint8(img_hsv[:, :, 1] / np.log(- value + np.e))
        self.last_image = self.current_img
        self.current_img = cv2.cvtColor(img_hsv, cv2.COLOR_HLS2BGR)

# 人脸识别 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLOR_BGR2HLS [as 别名]
def upsample_color_image(grayscale_highres, color_lowres_bgr, colorspace='LAB'):
    """
    Generate a high res color image from a high res grayscale image, and a low res color image,
    using the trick described in:
    http://www.planetary.org/blogs/emily-lakdawalla/2013/04231204-image-processing-colorizing-images.html
    """
    assert(len(grayscale_highres.shape) == 2)
    assert(len(color_lowres_bgr.shape) == 3 and color_lowres_bgr.shape[2] == 3)

    if colorspace == 'LAB':
        # convert color image to LAB space
        lab = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2LAB)
        # replace lightness channel with the highres image
        lab[:, :, 0] = grayscale_highres
        # convert back to BGR
        color_highres_bgr = cv2.cvtColor(src=lab, code=cv2.COLOR_LAB2BGR)
    elif colorspace == 'HSV':
        # convert color image to HSV space
        hsv = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2HSV)
        # replace value channel with the highres image
        hsv[:, :, 2] = grayscale_highres
        # convert back to BGR
        color_highres_bgr = cv2.cvtColor(src=hsv, code=cv2.COLOR_HSV2BGR)
    elif colorspace == 'HLS':
        # convert color image to HLS space
        hls = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2HLS)
        # replace lightness channel with the highres image
        hls[:, :, 1] = grayscale_highres
        # convert back to BGR
        color_highres_bgr = cv2.cvtColor(src=hls, code=cv2.COLOR_HLS2BGR)

    return color_highres_bgr