Python cv2.COLORMAP_HSV Examples

The following are 6 code examples of cv2.COLORMAP_HSV(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: misc.py    From LightNet with MIT License 6 votes vote down vote up 
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))
Example #2
Source File: misc_functions.py    From aerial_mtl with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
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))
Example #3
Source File: live_demo.py    From mr_saliency with GNU General Public License v2.0 6 votes vote down vote up 
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)
Example #4
Source File: util.py    From pymotutils with GNU General Public License v3.0 5 votes vote down vote up 
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)
Example #5
Source File: gms_matcher.py    From GMS-Feature-Matcher with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
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()
Example #6
Source File: opencv_demo.py    From GMS-Feature-Matcher with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
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