Python cv2.THRESH_TRUNC Examples
The following are 10
code examples of cv2.THRESH_TRUNC().
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Example #1
| Source File: preprocessing_transforms.py From ViP with MIT License | 6 votes |
|
def __init__(self):
self.resize = ResizeClip(resize_shape = [2,2])
self.crop = CropClip(0,0,0,0, crop_shape=[2,2])
self.rand_crop = RandomCropClip(crop_shape=[2,2])
self.cent_crop = CenterCropClip(crop_shape=[2,2])
self.rand_flip_h = RandomFlipClip(direction='h', p=1.0)
self.rand_flip_v = RandomFlipClip(direction='v', p=1.0)
self.rand_rot = RandomRotateClip(angles=[90])
self.rand_trans = RandomTranslateClip(translate=(0.5,0.5))
self.rand_zoom = RandomZoomClip(scale=(1.25,1.25))
self.sub_mean = SubtractMeanClip(clip_mean=np.zeros(1))
self.applypil = ApplyToPIL(transform=torchvision.transforms.ColorJitter, class_kwargs=dict(brightness=1))
self.applypil2 = ApplyToPIL(transform=torchvision.transforms.FiveCrop, class_kwargs=dict(size=(64,64)))
self.applytensor = ApplyToTensor(transform=torchvision.transforms.Normalize, class_kwargs=dict(mean=torch.tensor([0.,0.,0.]), std=torch.tensor([1.,1.,1.])))
self.applycv = ApplyOpenCV(transform=cv2.threshold, class_kwargs=dict(thresh=100, maxval=100, type=cv2.THRESH_TRUNC))
self.preproc = PreprocTransform()
Example #2
| Source File: MeterReader.py From Pointer-meter-identification-and-reading with MIT License | 6 votes |
|
def binary_image(self,img):
# 应用5种不同的阈值方法
# ret, th1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
# ret, th2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
# ret, th3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
# ret, th4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
# ret, th5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)
# titles = ['Gray', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
# images = [img_gray, th1, th2, th3, th4, th5]
# 使用Matplotlib显示
# for i in range(6):
# plt.subplot(2, 3, i + 1)
# plt.imshow(images[i], 'gray')
# plt.title(titles[i], fontsize=8)
# plt.xticks([]), plt.yticks([]) # 隐藏坐标轴
# plt.show()
# Otsu阈值
_, th = cv2.threshold(img, 0, 255, cv2.THRESH_TOZERO + cv2.THRESH_OTSU)
cv2.imshow('Binary image', th)
return th
# 边缘检测
Example #3
| Source File: util.py From CrowdFlow with GNU General Public License v3.0 | 6 votes |
|
def flow2RGB(flow, max_flow_mag = 5):
""" Color-coded visualization of optical flow fields
# Arguments
flow: array of shape [:,:,2] containing optical flow
max_flow_mag: maximal expected flow magnitude used to normalize. If max_flow_mag < 0 the maximal
magnitude of the optical flow field will be used
"""
hsv_mat = np.ones(shape=(flow.shape[0], flow.shape[1], 3), dtype=np.float32) * 255
ee = cv2.sqrt(flow[:, :, 0] * flow[:, :, 0] + flow[:, :, 1] * flow[:, :, 1])
angle = np.arccos(flow[:, :, 0]/ ee)
angle[flow[:, :, 0] == 0] = 0
angle[flow[:, :, 1] == 0] = 6.2831853 - angle[flow[:, :, 1] == 0]
angle = angle * 180 / 3.141
hsv_mat[:,:,0] = angle
if max_flow_mag < 0:
max_flow_mag = ee.max()
hsv_mat[:,:,1] = ee * 255.0 / max_flow_mag
ret, hsv_mat[:,:,1] = cv2.threshold(src=hsv_mat[:,:,1], maxval=255, thresh=255, type=cv2.THRESH_TRUNC )
rgb_mat = cv2.cvtColor(hsv_mat.astype(np.uint8), cv2.COLOR_HSV2BGR)
return rgb_mat
Example #4
| Source File: util.py From CrowdFlow with GNU General Public License v3.0 | 6 votes |
|
def flow2RGB(flow, max_flow_mag = 5):
""" Color-coded visualization of optical flow fields
# Arguments
flow: array of shape [:,:,2] containing optical flow
max_flow_mag: maximal expected flow magnitude used to normalize. If max_flow_mag < 0 the maximal
magnitude of the optical flow field will be used
"""
hsv_mat = np.ones(shape=(flow.shape[0], flow.shape[1], 3), dtype=np.float32) * 255
ee = cv2.sqrt(flow[:, :, 0] * flow[:, :, 0] + flow[:, :, 1] * flow[:, :, 1])
angle = np.arccos(flow[:, :, 0]/ ee)
angle[flow[:, :, 0] == 0] = 0
angle[flow[:, :, 1] == 0] = 6.2831853 - angle[flow[:, :, 1] == 0]
angle = angle * 180 / 3.141
hsv_mat[:,:,0] = angle
if max_flow_mag < 0:
max_flow_mag = ee.max()
hsv_mat[:,:,1] = ee * 220.0 / max_flow_mag
ret, hsv_mat[:,:,1] = cv2.threshold(src=hsv_mat[:,:,1], maxval=255, thresh=255, type=cv2.THRESH_TRUNC )
rgb_mat = cv2.cvtColor(hsv_mat.astype(np.uint8), cv2.COLOR_HSV2BGR)
return rgb_mat
Example #5
| Source File: OTSUThresholding.py From Finger-Detection-and-Tracking with BSD 2-Clause "Simplified" License | 6 votes |
|
def main():
threshold = 0
max_value = 255
image = cv2.imread("../data/7.1.08.tiff", 0)
# when applying OTSU threshold, set threshold to 0.
_, output1 = cv2.threshold(image, threshold, max_value, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
_, output2 = cv2.threshold(image, threshold, max_value, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
_, output3 = cv2.threshold(image, threshold, max_value, cv2.THRESH_TOZERO + cv2.THRESH_OTSU)
_, output4 = cv2.threshold(image, threshold, max_value, cv2.THRESH_TOZERO_INV + cv2.THRESH_OTSU)
_, output5 = cv2.threshold(image, threshold, max_value, cv2.THRESH_TRUNC + cv2.THRESH_OTSU)
images = [image, output1, output2, output3, output4, output5]
titles = ["Orignals", "Binary", "Binary Inverse", "TOZERO", "TOZERO INV", "TRUNC"]
for i in range(6):
plt.subplot(3, 2, i + 1)
plt.imshow(images[i], cmap='gray')
plt.title(titles[i])
plt.show()
Example #6
| Source File: corruptions.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.65, 0.3, 4, 0.69, 0.6, 0),
(0.65, 0.3, 3, 0.68, 0.6, 0),
(0.65, 0.3, 2, 0.68, 0.5, 0),
(0.65, 0.3, 1, 0.65, 1.5, 1),
(0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #7
| Source File: make_imagenet_c.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.65, 0.3, 4, 0.69, 0.6, 0),
(0.65, 0.3, 3, 0.68, 0.6, 0),
(0.65, 0.3, 2, 0.68, 0.5, 0),
(0.65, 0.3, 1, 0.65, 1.5, 1),
(0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #8
| Source File: make_cifar_c.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.62,0.1,0.7,0.7,0.5,0),
(0.65,0.1,0.8,0.7,0.5,0),
(0.65,0.3,1,0.69,0.5,0),
(0.65,0.1,0.7,0.69,0.6,1),
(0.65,0.1,0.5,0.68,0.6,1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #9
| Source File: make_tinyimagenet_c.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.62,0.1,0.7,0.7,0.6,0),
(0.65,0.1,0.8,0.7,0.6,0),
(0.65,0.3,1,0.69,0.6,0),
(0.65,0.1,0.7,0.68,0.6,1),
(0.65,0.1,0.5,0.67,0.6,1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #10
| Source File: make_imagenet_c_inception.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.65,0.3,4,0.69,0.9,0),
(0.65,0.3,3.5,0.68,0.9,0),
(0.65,0.3,3,0.68,0.8,0),
(0.65,0.3,1.2,0.65,1.8,1),
(0.67,0.4,1.2,0.65,1.8,1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
