Python cv2.COLORMAP_HSV属性代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_HSV [as 别名]
def save_class_activation_on_image(org_img, activation_map, file_name):
    """
        Saves cam activation map and activation map on the original image

    Args:
        org_img (PIL img): Original image
        activation_map (numpy arr): activation map (grayscale) 0-255
        file_name (str): File name of the exported image
    """
    if not os.path.exists('../results'):
        os.makedirs('../results')
    # Grayscale activation map
    path_to_file = os.path.join('../results', file_name+'_Cam_Grayscale.jpg')
    cv2.imwrite(path_to_file, activation_map)
    # Heatmap of activation map
    activation_heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_HSV)
    path_to_file = os.path.join('../results', file_name+'_Cam_Heatmap.jpg')
    cv2.imwrite(path_to_file, activation_heatmap)
    # Heatmap on picture
    org_img = cv2.resize(org_img, (224, 224))
    img_with_heatmap = np.float32(activation_heatmap) + np.float32(org_img)
    img_with_heatmap = img_with_heatmap / np.max(img_with_heatmap)
    path_to_file = os.path.join('../results', file_name+'_Cam_On_Image.jpg')
    cv2.imwrite(path_to_file, np.uint8(255 * img_with_heatmap)) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_HSV [as 别名]
def save_class_activation_on_image(org_img, activation_map, file_name):
    """
        Saves cam activation map and activation map on the original image

    Args:
        org_img (PIL img): Original image
        activation_map (numpy arr): activation map (grayscale) 0-255
        file_name (str): File name of the exported image
    """
    # Grayscale activation map
    path_to_file = os.path.join('../results', file_name+'_Cam_Grayscale.jpg')
    cv2.imwrite(path_to_file, activation_map)
    # Heatmap of activation map
    activation_heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_HSV)
    path_to_file = os.path.join('../results', file_name+'_Cam_Heatmap.jpg')
    cv2.imwrite(path_to_file, activation_heatmap)
    # Heatmap on picture
    org_img = cv2.resize(org_img, (224, 224))
    img_with_heatmap = np.float32(activation_heatmap) + np.float32(org_img)
    img_with_heatmap = img_with_heatmap / np.max(img_with_heatmap)
    path_to_file = os.path.join('../results', file_name+'_Cam_On_Image.jpg')
    cv2.imwrite(path_to_file, np.uint8(255 * img_with_heatmap)) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_HSV [as 别名]
def __init__(self, parent, capture, fps=24):
        wx.Panel.__init__(self, parent)
                
        self.capture = capture2
        ret, frame = self.capture.read()


        sal = mr_sal.saliency(frame)
        sal = cv2.resize(sal,(320,240)).astype(sp.uint8)
        sal = cv2.normalize(sal, None, 0, 255, cv2.NORM_MINMAX)
        outsal = cv2.applyColorMap(sal,cv2.COLORMAP_HSV)
        self.bmp = wx.BitmapFromBuffer(320,240, outsal.astype(sp.uint8))

        self.timer = wx.Timer(self)
        self.timer.Start(1000./fps)

        self.Bind(wx.EVT_PAINT, self.OnPaint)
        self.Bind(wx.EVT_TIMER, self.NextFrame) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_HSV [as 别名]
def apply_heat_map_uchar(values, mini=None, maxi=None):
    """Color values by their intensity.

    Applies an HSV color map.

    Parameters
    ----------
    values: ndarray
        The N dimensional array of intensities (ndim=1).
    mini : Optional[float]
        The intensity value of minimum saturation (lower bound of color map).
    maxi : Optional[float]
        The intensity value of maximum saturation (upper bound of color map).

    Returns
    -------
    ndarray
        The Nx3 shaped array of color codes in range [0, 255]. The dtype is
        np.int.

    """
    if len(values) == 0:
        return np.zeros((0, ), dtype=np.uint8)
    mini, maxi = mini or np.min(values), maxi or np.max(values)
    valrange = maxi - mini
    if valrange < np.finfo(valrange).eps:
        valrange = np.inf
    normalized = (255. * (values - mini) / valrange).astype(np.uint8)
    colors = cv2.applyColorMap(normalized, cv2.COLORMAP_HSV)
    return colors.astype(np.int).reshape(-1, 3) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_HSV [as 别名]
def draw_matches(self, src1, src2, drawing_type):
        height = max(src1.shape[0], src2.shape[0])
        width = src1.shape[1] + src2.shape[1]
        output = np.zeros((height, width, 3), dtype=np.uint8)
        output[0:src1.shape[0], 0:src1.shape[1]] = src1
        output[0:src2.shape[0], src1.shape[1]:] = src2[:]

        if drawing_type == DrawingType.ONLY_LINES:
            for i in range(len(self.gms_matches)):
                left = self.keypoints_image1[self.gms_matches[i].queryIdx].pt
                right = tuple(sum(x) for x in zip(self.keypoints_image2[self.gms_matches[i].trainIdx].pt, (src1.shape[1], 0)))
                cv2.line(output, tuple(map(int, left)), tuple(map(int, right)), (0, 255, 255))

        elif drawing_type == DrawingType.LINES_AND_POINTS:
            for i in range(len(self.gms_matches)):
                left = self.keypoints_image1[self.gms_matches[i].queryIdx].pt
                right = tuple(sum(x) for x in zip(self.keypoints_image2[self.gms_matches[i].trainIdx].pt, (src1.shape[1], 0)))
                cv2.line(output, tuple(map(int, left)), tuple(map(int, right)), (255, 0, 0))

            for i in range(len(self.gms_matches)):
                left = self.keypoints_image1[self.gms_matches[i].queryIdx].pt
                right = tuple(sum(x) for x in zip(self.keypoints_image2[self.gms_matches[i].trainIdx].pt, (src1.shape[1], 0)))
                cv2.circle(output, tuple(map(int, left)), 1, (0, 255, 255), 2)
                cv2.circle(output, tuple(map(int, right)), 1, (0, 255, 0), 2)

        elif drawing_type == DrawingType.COLOR_CODED_POINTS_X or drawing_type == DrawingType.COLOR_CODED_POINTS_Y or drawing_type == DrawingType.COLOR_CODED_POINTS_XpY :
            _1_255 = np.expand_dims( np.array( range( 0, 256 ), dtype='uint8' ), 1 )
            _colormap = cv2.applyColorMap(_1_255, cv2.COLORMAP_HSV)

            for i in range(len(self.gms_matches)):
                left = self.keypoints_image1[self.gms_matches[i].queryIdx].pt
                right = tuple(sum(x) for x in zip(self.keypoints_image2[self.gms_matches[i].trainIdx].pt, (src1.shape[1], 0)))

                if drawing_type == DrawingType.COLOR_CODED_POINTS_X:
                    colormap_idx = int(left[0] * 256. / src1.shape[1] ) # x-gradient
                if drawing_type == DrawingType.COLOR_CODED_POINTS_Y:
                    colormap_idx = int(left[1] * 256. / src1.shape[0] ) # y-gradient
                if drawing_type == DrawingType.COLOR_CODED_POINTS_XpY:
                    colormap_idx = int( (left[0] - src1.shape[1]*.5 + left[1] - src1.shape[0]*.5) * 256. / (src1.shape[0]*.5 + src1.shape[1]*.5) ) # manhattan gradient

                color = tuple( map(int, _colormap[ colormap_idx,0,: ]) )
                cv2.circle(output, tuple(map(int, left)), 1, color, 2)
                cv2.circle(output, tuple(map(int, right)), 1, color, 2)


        cv2.imshow('show', output)
        cv2.waitKey() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_HSV [as 别名]
def draw_matches(src1, src2, kp1, kp2, matches, drawing_type):
    height = max(src1.shape[0], src2.shape[0])
    width = src1.shape[1] + src2.shape[1]
    output = np.zeros((height, width, 3), dtype=np.uint8)
    output[0:src1.shape[0], 0:src1.shape[1]] = src1
    output[0:src2.shape[0], src1.shape[1]:] = src2[:]

    if drawing_type == DrawingType.ONLY_LINES:
        for i in range(len(matches)):
            left = kp1[matches[i].queryIdx].pt
            right = tuple(sum(x) for x in zip(kp2[matches[i].trainIdx].pt, (src1.shape[1], 0)))
            cv2.line(output, tuple(map(int, left)), tuple(map(int, right)), (0, 255, 255))

    elif drawing_type == DrawingType.LINES_AND_POINTS:
        for i in range(len(matches)):
            left = kp1[matches[i].queryIdx].pt
            right = tuple(sum(x) for x in zip(kp2[matches[i].trainIdx].pt, (src1.shape[1], 0)))
            cv2.line(output, tuple(map(int, left)), tuple(map(int, right)), (255, 0, 0))

        for i in range(len(matches)):
            left = kp1[matches[i].queryIdx].pt
            right = tuple(sum(x) for x in zip(kp2[matches[i].trainIdx].pt, (src1.shape[1], 0)))
            cv2.circle(output, tuple(map(int, left)), 1, (0, 255, 255), 2)
            cv2.circle(output, tuple(map(int, right)), 1, (0, 255, 0), 2)

    elif drawing_type == DrawingType.COLOR_CODED_POINTS_X or drawing_type == DrawingType.COLOR_CODED_POINTS_Y or drawing_type == DrawingType.COLOR_CODED_POINTS_XpY:
        _1_255 = np.expand_dims(np.array(range(0, 256), dtype='uint8'), 1)
        _colormap = cv2.applyColorMap(_1_255, cv2.COLORMAP_HSV)

        for i in range(len(matches)):
            left = kp1[matches[i].queryIdx].pt
            right = tuple(sum(x) for x in zip(kp2[matches[i].trainIdx].pt, (src1.shape[1], 0)))

            if drawing_type == DrawingType.COLOR_CODED_POINTS_X:
                colormap_idx = int(left[0] * 256. / src1.shape[1])  # x-gradient
            if drawing_type == DrawingType.COLOR_CODED_POINTS_Y:
                colormap_idx = int(left[1] * 256. / src1.shape[0])  # y-gradient
            if drawing_type == DrawingType.COLOR_CODED_POINTS_XpY:
                colormap_idx = int((left[0] - src1.shape[1]*.5 + left[1] - src1.shape[0]*.5) * 256. / (src1.shape[0]*.5 + src1.shape[1]*.5))  # manhattan gradient

            color = tuple(map(int, _colormap[colormap_idx, 0, :]))
            cv2.circle(output, tuple(map(int, left)), 1, color, 2)
            cv2.circle(output, tuple(map(int, right)), 1, color, 2)
    return output