Python cv2.pow() Examples

The following are 9 code examples of cv2.pow(). 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: train.py    From sp-society-camera-model-identification with GNU General Public License v3.0 6 votes vote down vote up 
def get_random_manipulation(img, manipulation=None):

    if manipulation == None:
        manipulation = random.choice(MANIPULATIONS)

    if manipulation.startswith('jpg'):
        quality = int(manipulation[3:])
        out = BytesIO()
        im = Image.fromarray(img)
        im.save(out, format='jpeg', quality=quality)
        im_decoded = jpeg.JPEG(np.frombuffer(out.getvalue(), dtype=np.uint8)).decode()
        del out
        del im
    elif manipulation.startswith('gamma'):
        gamma = float(manipulation[5:])
        # alternatively use skimage.exposure.adjust_gamma
        # img = skimage.exposure.adjust_gamma(img, gamma)
        im_decoded = np.uint8(cv2.pow(img / 255., gamma)*255.)
    elif manipulation.startswith('bicubic'):
        scale = float(manipulation[7:])
        im_decoded = cv2.resize(img,(0,0), fx=scale, fy=scale, interpolation = cv2.INTER_CUBIC)
    else:
        assert False
    return im_decoded, MANIPULATIONS.index(manipulation)
Example #2
Source File: FingerDetection.py    From Finger-Detection-and-Tracking with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def farthest_point(defects, contour, centroid):
    if defects is not None and centroid is not None:
        s = defects[:, 0][:, 0]
        cx, cy = centroid

        x = np.array(contour[s][:, 0][:, 0], dtype=np.float)
        y = np.array(contour[s][:, 0][:, 1], dtype=np.float)

        xp = cv2.pow(cv2.subtract(x, cx), 2)
        yp = cv2.pow(cv2.subtract(y, cy), 2)
        dist = cv2.sqrt(cv2.add(xp, yp))

        dist_max_i = np.argmax(dist)

        if dist_max_i < len(s):
            farthest_defect = s[dist_max_i]
            farthest_point = tuple(contour[farthest_defect][0])
            return farthest_point
        else:
            return None
Example #3
Source File: dataset_util.py    From LaneSegmentationNetwork with GNU Lesser General Public License v3.0 6 votes vote down vote up 
def convert_to_nearest_label(label_path, image_size, apply_ignore=True):
    """
    Convert RGB label image to onehot label image
    :param label_path: File path of RGB label image
    :param image_size: Size to resize result image
    :param apply_ignore: Apply ignore
    :return:
    """
    label = np.array(Image.open(label_path).resize((image_size[0], image_size[1]), Image.ANTIALIAS))[:, :, :3]
    label = label.astype(np.float32)
    stacked_label = list()
    for index, mask in enumerate(label_mask):
        length = np.sum(cv2.pow(label - mask, 2), axis=2, keepdims=False)
        length = cv2.sqrt(length)
        stacked_label.append(length)

    stacked_label = np.array(stacked_label)
    stacked_label = np.transpose(stacked_label, [1, 2, 0])
    converted_to_classes = np.argmin(stacked_label, axis=2).astype(np.uint8)
    if apply_ignore:
        ignore_mask = (converted_to_classes == (len(label_mask) - 1)).astype(np.uint8)
        ignore_mask *= (256 - len(label_mask))
        converted_to_classes += ignore_mask

    return converted_to_classes
Example #4
Source File: im_transform.py    From Traffic_sign_detection_YOLO with MIT License 5 votes vote down vote up 
def imcv2_recolor(im, a = .1):
	t = [np.random.uniform()]
	t += [np.random.uniform()]
	t += [np.random.uniform()]
	t = np.array(t) * 2. - 1.

	# random amplify each channel
	im = im * (1 + t * a)
	mx = 255. * (1 + a)
	up = np.random.uniform() * 2 - 1
# 	im = np.power(im/mx, 1. + up * .5)
	im = cv2.pow(im/mx, 1. + up * .5)
	return np.array(im * 255., np.uint8)
Example #5
Source File: im_transform.py    From Automatic-Identification-and-Counting-of-Blood-Cells with GNU General Public License v3.0 5 votes vote down vote up 
def imcv2_recolor(im, a=.1):
    t = [np.random.uniform()]
    t += [np.random.uniform()]
    t += [np.random.uniform()]
    t = np.array(t) * 2. - 1.

    # random amplify each channel
    im = im * (1 + t * a)
    mx = 255. * (1 + a)
    up = np.random.uniform() * 2 - 1
    # 	im = np.power(im/mx, 1. + up * .5)
    im = cv2.pow(im / mx, 1. + up * .5)
    return np.array(im * 255., np.uint8)
Example #6
Source File: im_transform.py    From Traffic-Signs-and-Object-Detection with GNU General Public License v3.0 5 votes vote down vote up 
def imcv2_recolor(im, a = .1):
	t = [np.random.uniform()]
	t += [np.random.uniform()]
	t += [np.random.uniform()]
	t = np.array(t) * 2. - 1.

	# random amplify each channel
	im = im * (1 + t * a)
	mx = 255. * (1 + a)
	up = np.random.uniform() * 2 - 1
# 	im = np.power(im/mx, 1. + up * .5)
	im = cv2.pow(im/mx, 1. + up * .5)
	return np.array(im * 255., np.uint8)
Example #7
Source File: im_transform.py    From YOLO_Object_Detection with GNU General Public License v3.0 5 votes vote down vote up 
def imcv2_recolor(im, a = .1):
	t = [np.random.uniform()]
	t += [np.random.uniform()]
	t += [np.random.uniform()]
	t = np.array(t) * 2. - 1.

	# random amplify each channel
	im = im * (1 + t * a)
	mx = 255. * (1 + a)
	up = np.random.uniform() * 2 - 1
# 	im = np.power(im/mx, 1. + up * .5)
	im = cv2.pow(im/mx, 1. + up * .5)
	return np.array(im * 255., np.uint8)
Example #8
Source File: augmentation.py    From camera_identification with MIT License 5 votes vote down vote up 
def _gamma_manip(image, gamma):
        result = np.uint8(cv2.pow(image / 255., gamma) * 255.)
        return result
Example #9
Source File: im_transform.py    From darkflow with GNU General Public License v3.0 4 votes vote down vote up 
def imcv2_recolor(im, a = .1):
	t = [np.random.uniform()]
	t += [np.random.uniform()]
	t += [np.random.uniform()]
	t = np.array(t) * 2. - 1.

	# random amplify each channel
	im = im * (1 + t * a)
	mx = 255. * (1 + a)
	up = np.random.uniform() * 2 - 1
# 	im = np.power(im/mx, 1. + up * .5)
	im = cv2.pow(im/mx, 1. + up * .5)
	return np.array(im * 255., np.uint8)