我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用cv2.LUT。
def test_sun_rgbd(): from pybot.vision.image_utils import to_color from pybot.vision.imshow_utils import imshow_cv from pybot.utils.io_utils import write_video from pybot.vision.color_utils import colormap directory = '/media/HD1/data/SUNRGBD/' dataset = SUNRGBDDataset(directory) colors = cv2.imread('data/sun3d/sun.png').astype(np.uint8) for (rgb, depth, label) in dataset.segmentationdb(None): cout = np.dstack([label, label, label]) colored = cv2.LUT(cout, colors) cdepth = colormap(depth / 64000.0) for j in range(5): write_video('xtion.avi', np.hstack([rgb, cdepth, colored])) # for f in dataset.iteritems(every_k_frames=5): # # vis = rgbd_data_uw.annotate(f) # imshow_cv('frame', f.img, text='Image') # imshow_cv('depth', (f.depth / 16).astype(np.uint8), text='Depth') # imshow_cv('instance', (f.instance).astype(np.uint8), text='Instance') # imshow_cv('label', (f.label).astype(np.uint8), text='Label') # cv2.waitKey(100) return dataset
def adjust_gamma(image, gamma=1.0): # build a lookup table mapping the pixel values [0, 255] to # their adjusted gamma values invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table return cv2.LUT(image, table) #Random noize effect
def adjust_gamma(self, image, gamma=1.0): # build a lookup table mapping the pixel values [0, 255] to # their adjusted gamma values invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table return cv2.LUT(image, table)
def applyColorMap(gray, cmap='flame'): ''' like cv2.applyColorMap(im_gray, cv2.COLORMAP_*) but with different color maps ''' # TODO:implement more cmaps if cmap != 'flame': raise NotImplemented # TODO: make better mx = 256 # if gray.dtype==np.uint8 else 65535 lut = np.empty(shape=(256, 3)) cmap = ( # taken from pyqtgraph GradientEditorItem (0, (0, 0, 0)), (0.2, (7, 0, 220)), (0.5, (236, 0, 134)), (0.8, (246, 246, 0)), (1.0, (255, 255, 255)) ) # build lookup table: lastval, lastcol = cmap[0] for step, col in cmap[1:]: val = int(step * mx) for i in range(3): lut[lastval:val, i] = np.linspace( lastcol[i], col[i], val - lastval) lastcol = col lastval = val s0, s1 = gray.shape out = np.empty(shape=(s0, s1, 3), dtype=np.uint8) for i in range(3): out[..., i] = cv2.LUT(gray, lut[:, i]) return out
def adjust_gamma(img, gamma=1.0): assert (img.shape[0] == 1) invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") new_img = cv2.LUT(np.array(img, dtype=np.uint8), table) return new_img
def adjust_gamma(image, gamma=1.0): """ http://www.pyimagesearch.com/2015/10/05/opencv-gamma-correction/ """ # build a lookup table mapping the pixel values [0, 255] to # their adjusted gamma values invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table return cv2.LUT(image, table)
def gamma_transform(img, gamma): gamma_table = [np.power(x / 255.0, gamma) * 255.0 for x in range(256)] gamma_table = np.round(np.array(gamma_table)).astype(np.uint8) return cv2.LUT(img, gamma_table)
def adjust_gamma(image, gamma=1.0): """Adjust gamma value of image Keyword arguments: imgs_0 -- first image of image pair (with length of bath size) gamma -- gamma value (default = 1.0) """ invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table return cv2.LUT(image, table)
def adjust_gamma(imgs, gamma=1.0): assert (len(imgs.shape)==4) #4D arrays assert (imgs.shape[1]==1) #check the channel is 1 # build a lookup table mapping the pixel values [0, 255] to # their adjusted gamma values invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table new_imgs = np.empty(imgs.shape) for i in range(imgs.shape[0]): new_imgs[i,0] = cv2.LUT(np.array(imgs[i,0], dtype = np.uint8), table) return new_imgs
def equalize_hist(image): hist, bins = np.histogram(image.flatten(), 256, [0, 256]) cdf = hist.cumsum() # ??????? cdf_m = np.ma.masked_equal(cdf, 0) # ???????0? cdf_m = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min()) # ????????lut[i] = int(255.0 *p[i])?? cdf = np.ma.filled(cdf_m, 0).astype('uint8') # ???????????0 return cv2.LUT(image, cdf)
def _augment(self, img, gamma): lut = ((np.arange(256, dtype='float32') / 255) ** (1. / (1. + gamma)) * 255).astype('uint8') img = np.clip(img, 0, 255).astype('uint8') img = cv2.LUT(img, lut).astype('float32') return img
def render(self, img_rgb): # warming filter: increase red, decrease blue c_r, c_g, c_b = cv2.split(img_rgb) c_r = cv2.LUT(c_r, self.incr_ch_lut).astype(np.uint8) c_b = cv2.LUT(c_b, self.decr_ch_lut).astype(np.uint8) img_rgb = cv2.merge((c_r, c_g, c_b)) # increase color saturation c_h, c_s, c_v = cv2.split(cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)) c_s = cv2.LUT(c_s, self.incr_ch_lut).astype(np.uint8) return cv2.cvtColor(cv2.merge((c_h, c_s, c_v)), cv2.COLOR_HSV2RGB)
def render(self, img_rgb): # cooling filter: increase blue, decrease red c_r, c_g, c_b = cv2.split(img_rgb) c_r = cv2.LUT(c_r, self.decr_ch_lut).astype(np.uint8) c_b = cv2.LUT(c_b, self.incr_ch_lut).astype(np.uint8) img_rgb = cv2.merge((c_r, c_g, c_b)) # decrease color saturation c_h, c_s, c_v = cv2.split(cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)) c_s = cv2.LUT(c_s, self.decr_ch_lut).astype(np.uint8) return cv2.cvtColor(cv2.merge((c_h, c_s, c_v)), cv2.COLOR_HSV2RGB)
def random_brightness(image, alpha=2.0): gamma = np.random.rand() * alpha gf = [[255 * pow(i/255, 1/gamma)] for i in range(256)] table = np.reshape(gf, (256, -1)) image = cv2.LUT(image, table) return image
