def test_bgr2hsv(self):
in_img = np.random.rand(10, 10, 3).astype(np.float32)
out_img = mmcv.bgr2hsv(in_img)
argmax = in_img.argmax(axis=2)
computed_hsv = np.empty_like(in_img, dtype=in_img.dtype)
for i in range(in_img.shape[0]):
for j in range(in_img.shape[1]):
b = in_img[i, j, 0]
g = in_img[i, j, 1]
r = in_img[i, j, 2]
v = max(r, g, b)
s = (v - min(r, g, b)) / v if v != 0 else 0
if argmax[i, j] == 0:
h = 240 + 60 * (r - g) / (v - min(r, g, b))
elif argmax[i, j] == 1:
h = 120 + 60 * (b - r) / (v - min(r, g, b))
else:
h = 60 * (g - b) / (v - min(r, g, b))
if h < 0:
h += 360
computed_hsv[i, j, :] = [h, s, v]
assert_array_almost_equal(out_img, computed_hsv, decimal=2)
def __call__(self, results):
"""Call function to perform photometric distortion on images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with images distorted.
"""
img = results['img']
# random brightness
img = self.brightness(img)
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
img = self.contrast(img)
hsv_mode = random.randint(4)
if hsv_mode:
# random saturation/hue distortion
img = mmcv.bgr2hsv(img)
if hsv_mode == 1 or hsv_mode == 3:
img = self.saturation(img)
if hsv_mode == 2 or hsv_mode == 3:
img = self.hue(img)
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
img = self.contrast(img)
# randomly swap channels
self.swap_channels(img)
results['img'] = img
return results
def __call__(self, results):
img = results['img']
if 'ref_img' in results:
ref_img = results['ref_img']
else:
ref_img = None
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
if ref_img is not None:
ref_img += delta
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
if ref_img is not None:
ref_img *= alpha
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
if ref_img is not None:
ref_img = mmcv.bgr2hsv(ref_img)
# random saturation
if random.randint(2):
random_saturation = random.uniform(self.saturation_lower,
self.saturation_upper)
img[..., 1] *= random_saturation
if ref_img is not None:
ref_img[..., 1] *= random_saturation
# random hue
if random.randint(2):
random_hue = random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0] += random_hue
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
if ref_img is not None:
ref_img[..., 0] += random_hue
ref_img[..., 0][ref_img[..., 0] > 360] -= 360
ref_img[..., 0][ref_img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
if ref_img is not None:
ref_img = mmcv.hsv2bgr(ref_img)
# random contrast
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
if ref_img is not None:
ref_img *= alpha
# randomly swap channels
if random.randint(2):
random_permute = random.permutation(3)
img = img[..., random_permute]
if ref_img is not None:
ref_img = ref_img[..., random_permute]
results['img'] = img
if ref_img is not None:
results['ref_img'] = ref_img
return results
def __call__(self, img, boxes, labels, masks=None):
if self.color_choose == 0:
if random.uniform() < self.gray_p:
gray = mmcv.bgr2gray(img)
img = mmcv.gray2bgr(gray)
return img, boxes, labels, masks
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
# random saturation
if random.randint(2):
img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
# random hue
if random.randint(2):
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
# randomly swap channels
if random.randint(2):
img = img[..., random.permutation(3)]
else:
if self.color_choose == 1:
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
elif self.color_choose == 2:
# random contrast first
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
else:
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
if self.color_choose == 3:
# random saturation
if random.randint(2):
img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
if self.color_choose == 4:
# random hue
if random.randint(2):
img[..., 0] += random.uniform(-self.hue_delta,
self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
return img, boxes, labels, masks
def __call__(self, results):
print('start')
import copy
img = copy.deepcopy(results['img'])
print(img.shape)
img[0,0,0]+=1
print('loaded')
# random brightness
if random.randint(2):
print('a')
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
print(delta)
print(img.shape)
print('success')
print(delta.shape)
img += delta
print('exit')
print('first')
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
if random.randint(2):
print('exit1')
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
print(img.shape)
# convert color from BGR to HSV
print('bgr')
img = mmcv.bgr2hsv(img)
print('mid')
# random saturation
if random.randint(2):
img[..., 1] *= 2#random.uniform(self.saturation_lower,
#self.saturation_upper)
print('exit2')
# random hue
if random.randint(2):
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
print(img.shape)
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
print(img.shape)
# randomly swap channels
if random.randint(2):
img = img[..., random.permutation(3)]
results['img'] = img
print('end')
return results
def __call__(self, results):
print('photo metric distortion')
i += 1
img = results['img']
template_img = results['template_img']
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
template_img += delta
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
template_img *= alpha
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
template_img = mmcv.bgr2hsv(template_img)
# random saturation
if random.randint(2):
i += 1
img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
template_img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
# random hue
if random.randint(2):
i += 1
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
template_img[..., 0] += random.uniform(-self.hue_delta,
self.hue_delta)
template_img[..., 0][template_img[..., 0] > 360] -= 360
template_img[..., 0][template_img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
template_img = mmcv.hsv2bgr(template_img)
# random contrast
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
template_img *= alpha
# randomly swap channels
if random.randint(2):
i += 1
img = img[..., random.permutation(3)]
template_img = template_img[..., random.permutation(3)]
results['img'] = img
results['template_img'] = template_img
return results
def __call__(self, results):
"""Call function to perform photometric distortion on images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with images distorted.
"""
if 'img_fields' in results:
assert results['img_fields'] == ['img'], \
'Only single img_fields is allowed'
img = results['img']
assert img.dtype == np.float32, \
'PhotoMetricDistortion needs the input image of dtype np.float32,'\
' please set "to_float32=True" in "LoadImageFromFile" pipeline'
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
# random saturation
if random.randint(2):
img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
# random hue
if random.randint(2):
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
# randomly swap channels
if random.randint(2):
img = img[..., random.permutation(3)]
results['img'] = img
return results
def __call__(self, results, state=None):
if state is None:
delta_randint = random.randint(2)
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
mode = random.randint(2)
mode_randint = random.randint(2)
alpha = random.uniform(self.contrast_lower, self.contrast_upper)
sat_randint = random.randint(2)
sat_multiplier = random.uniform(self.saturation_lower,
self.saturation_upper)
hue_randint = random.randint(2)
hue_multiplier = random.uniform(-self.hue_delta, self.hue_delta)
contrast_randint = random.randint(2)
contrast_alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
perm_randint = random.randint(2)
perm = random.permutation(3)
state = dict(delta_randint=delta_randint,
delta=delta,
mode=mode,
mode_randint=mode_randint,
alpha=alpha,
sat_randint=sat_randint,
sat_multiplier=sat_multiplier,
hue_randint=hue_randint,
hue_multiplier=hue_multiplier,
contrast_randint=contrast_randint,
contrast_alpha=contrast_alpha,
perm_randint=perm_randint,
perm=perm)
else:
delta_randint = state['delta_randint']
delta = state['delta']
mode = state['mode']
mode_randint = state['mode_randint']
alpha = state['alpha']
sat_randint = state['sat_randint']
sat_multiplier = state['sat_multiplier']
hue_randint = state['hue_randint']
hue_multiplier = state['hue_multiplier']
contrast_randint = state['contrast_randint']
contrast_alpha = state['contrast_alpha']
perm_randint = state['perm_randint']
perm = state['perm']
img = results['img']
# random brightness
if delta_randint:
delta = delta
img += delta
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = mode
if mode == 1:
if mode_randint:
alpha = alpha
img *= alpha
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
# random saturation
if sat_randint:
img[..., 1] *= sat_multiplier
# random hue
if hue_randint:
img[..., 0] += hue_multiplier
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
if contrast_randint:
alpha = contrast_alpha
img *= alpha
# randomly swap channels
if perm_randint:
img = img[..., perm]
results['img'] = img
return results, state
def random_photo_metric_distortion(self, img):
# https://github.com/open-mmlab/mmdetection/blob/a054aef422d650a644459bdc232248eefa8ae8b7/mmdet/datasets/extra_aug.py#L8
# brightness_delta=32
# contrast_range=(0.5, 1.5),
# saturation_range=(0.5, 1.5),
# hue_delta=18)
# brightness_delta = photo_metric_distortion['brightness_delta']
# contrast_lower, contrast_upper = photo_metric_distortion['contrast_range']
# saturation_lower, saturation_upper = photo_metric_distortion['saturation_range']
# hue_delta = self.photo_metric_distortion['hue_delta']
brightness_delta = 32
contrast_lower, contrast_upper = (0.5, 1.5)
saturation_lower, saturation_upper = (0.5, 1.5)
hue_delta = 18
# change datatype before do photo metric distortion
img = img.astype(np.float32)
# random brightness
if np.random.randint(2):
delta = np.random.uniform(-brightness_delta, brightness_delta)
img += delta
img = img.clip(min=0, max=255)
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = np.random.randint(2)
if mode == 1:
if np.random.randint(2):
alpha = np.random.uniform(contrast_lower, contrast_upper)
img *= alpha
img = img.clip(min=0, max=255)
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
# random saturation
if np.random.randint(2):
img[..., 1] *= np.random.uniform(saturation_lower,
saturation_upper)
img[..., 1] = img[..., 1].clip(min=0, max=1)
# random hue
if np.random.randint(2):
img[..., 0] += np.random.uniform(-hue_delta, hue_delta)
# img[..., 0][img[..., 0] > 360] -= 360
# img[..., 0][img[..., 0] < 0] += 360
img[..., 0] = img[..., 0].clip(min=0, max=360)
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
if np.random.randint(2):
alpha = np.random.uniform(contrast_lower, contrast_upper)
img *= alpha
img = img.clip(min=0, max=255)
# randomly swap channels
# if np.random.randint(2):
# img = img[..., np.random.permutation(3)]
return img.astype(np.uint8)
if flag_4:
# Color space conversion
"""
Supported conversion methods:
bgr2gray
gray2bgr
bgr2rgb
rgb2bgr
bgr2hsv
hsv2bgr
"""
img = mmcv.imread("asset/a.jpg")
img1 = mmcv.bgr2rgb(img)
img2 = mmcv.rgb2gray(img1)
img3 = mmcv.bgr2hsv(img)
mmcv.imshow(img1)
mmcv.imshow(img2)
mmcv.imshow(img3)
if flag_5:
# Resize
"""
There are three resize methods. All imresize_* methods have an argument return_scale, if this argument is False, then the return value is merely the resized image, otherwise is a tuple (resized_img, scale).
"""
img = mmcv.imread("asset/a.jpg")
dst_img = mmcv.imread("asset/b.jpg")
# resize to a given size
resized_img = mmcv.imresize(img, (1000, 600), return_scale=True)
mmcv.imshow(resized_img[0])
