def hsv(rgb):
hsv = clr.rgb2hsv(rgb)
hsv_c = clr.rgb2hsv(np.clip(rgb, 0.0, 1.0))
hsv_r = np.concatenate([x[..., np.newaxis] for x in [
hsv_c[:, :, 0], hsv_c[:, :, 1], hsv[:, :, 2]]], axis=-1)
rgb = clr.hsv2rgb(hsv_r)
return rgb
def hs_histogram(img, mask, bins=10, normed=False):
hsv = color.rgb2hsv(img)
maskedH = ma.array(hsv[:,:,0], mask=mask)
maskedS = ma.array(hsv[:,:,1], mask=mask)
histH, bin_edges_h = np.histogram(maskedH.compressed(), bins=bins, range=None, normed=normed)
histS, bin_edges_s = np.histogram(maskedS.compressed(), bins=bins, range=None, normed=normed)
return histH, histS
def __call__(self, img): hsv = color.rgb2hsv(img) hsv[:,:,2] = exposure.equalize_hist(hsv[:,:,2]) img = color.hsv2rgb(hsv) min_side = min(img.shape[:-1]) centre = img.shape[0] // 2, img.shape[1] // 2 img = img[centre[0] - min_side // 2:centre[0] + min_side // 2, centre[1] - min_side // 2:centre[1] + min_side // 2, :] img = transform.resize(img, (self.img_size, self.img_size)) img = np.rollaxis(img, -1) return img
def cv_hs_histogram_(img, mask):
hsv = color.rgb2hsv(img)
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
hist = cv2.calcHist(hsv, [0, 1], mask, hist_size, ranges)
return hist
def hs_histogram(img, mask, bins=10, normed=False):
hsv = color.rgb2hsv(img)
maskedH = ma.array(hsv[:, :, 0], mask=mask)
maskedS = ma.array(hsv[:, :, 1], mask=mask)
histH, bin_edges_h = np.histogram(maskedH.compressed(),
bins=bins,
range=None,
normed=normed)
histS, bin_edges_s = np.histogram(maskedS.compressed(),
bins=bins,
range=None,
normed=normed)
return histH, histS
def cv_hs_histogram_(img, mask):
hsv = color.rgb2hsv(img)
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
hist = cv2.calcHist(hsv, [0, 1], mask, hist_size, ranges)
return hist
def label_colormap(n_label=256, value=None):
"""Label colormap.
Parameters
----------
n_labels: int
Number of labels (default: 256).
value: float or int
Value scale or value of label color in HSV space.
Returns
-------
cmap: numpy.ndarray, (N, 3), numpy.uint8
Label id to colormap.
"""
def bitget(byteval, idx):
return (byteval & (1 << idx)) != 0
cmap = np.zeros((n_label, 3), dtype=np.uint8)
# for i in range(0, n_label):
# id = i
# r, g, b = 0, 0, 0
# for j in range(0, 8):
# r = np.bitwise_or(r, (bitget(id, 0) << 7 - j))
# g = np.bitwise_or(g, (bitget(id, 1) << 7 - j))
# b = np.bitwise_or(b, (bitget(id, 2) << 7 - j))
# id = id >> 3
# cmap[i, 0] = r
# cmap[i, 1] = g
# cmap[i, 2] = b
# 自己加的colormap 通道顺序为ｒｂｇ
cmap[1, :] = [255, 0, 0] # rope
# cmap[2, :] = [0, 255, 0] # cart
# cmap[3, :] = [0, 0, 255] # protected
# cmap[4, :] = [255, 255, 0] # hook
# cmap[5, :] = [255, 0, 255] # umbrella
if value is not None:
hsv = color_module.rgb2hsv(cmap.reshape(1, -1, 3))
if isinstance(value, float):
hsv[:, 1:, 2] = hsv[:, 1:, 2].astype(float) * value
else:
assert isinstance(value, int)
hsv[:, 1:, 2] = value
cmap = color_module.hsv2rgb(hsv).reshape(-1, 3)
return cmap
