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flowfunction.py
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flowfunction.py
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import mindspore
import mindspore.ops as O
import matplotlib.pyplot as plt
import numpy as np
import cv2
def make_colorwheel():
'''
Generates a color wheel for optical flow visualization as presented in:
Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
According to the C++ source code of Daniel Scharstein
According to the Matlab source code of Deqing Sun
'''
RY = 15
YG = 6
GC = 4
CB = 11
BM = 13
MR = 6
ncols = RY + YG + GC + CB + BM + MR
colorwheel = np.zeros((ncols, 3))
col = 0
# RY
colorwheel[0:RY, 0] = 255
colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
col = col+RY
# YG
colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
colorwheel[col:col+YG, 1] = 255
col = col+YG
# GC
colorwheel[col:col+GC, 1] = 255
colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
col = col+GC
# CB
colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
colorwheel[col:col+CB, 2] = 255
col = col+CB
# BM
colorwheel[col:col+BM, 2] = 255
colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
col = col+BM
# MR
colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
colorwheel[col:col+MR, 0] = 255
return colorwheel
def flow_compute_color(u, v, convert_to_bgr=False):
'''
Applies the flow color wheel to (possibly clipped) flow components u and v.
According to the C++ source code of Daniel Scharstein
According to the Matlab source code of Deqing Sun
:param u: np.ndarray, input horizontal flow
:param v: np.ndarray, input vertical flow
:param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB
:return:
'''
flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
colorwheel = make_colorwheel() # shape [55x3]
ncols = colorwheel.shape[0]
rad = np.sqrt(np.square(u) + np.square(v))
a = np.arctan2(-v, -u) / np.pi
fk = (a + 1) / 2 * (ncols - 1)
k0 = np.floor(fk).astype(np.int32)
k1 = k0 + 1
k1[k1 == ncols] = 0
f = fk - k0
for i in range(colorwheel.shape[1]):
tmp = colorwheel[:,i]
col0 = tmp[k0] / 255.0
col1 = tmp[k1] / 255.0
col = (1 - f) * col0 + f * col1
idx = (rad <= 1)
col[idx] = 1 - rad[idx] * (1-col[idx])
col[~idx] = col[~idx] * 0.75 # out of range?
# Note the 2-i => BGR instead of RGB
ch_idx = 2 - i if convert_to_bgr else i
flow_image[:, :, ch_idx] = np.floor(255 * col)
return flow_image
def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False):
'''
Expects a two dimensional flow image of shape [H,W,2]
According to the C++ source code of Daniel Scharstein
According to the Matlab source code of Deqing Sun
:param flow_uv: np.ndarray of shape [H,W,2]
:param clip_flow: float, maximum clipping value for flow
:return:
'''
assert flow_uv.ndim == 3, 'input flow must have three dimensions'
assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
if clip_flow is not None:
flow_uv = np.clip(flow_uv, 0, clip_flow)
u = flow_uv[:,:,0]
v = flow_uv[:,:,1]
rad = np.sqrt(np.square(u) + np.square(v))
rad_max = np.max(rad)
epsilon = 1e-5
u = u / (rad_max + epsilon)
v = v / (rad_max + epsilon)
return flow_compute_color(u, v, convert_to_bgr)
def flow_color(flow):
b = flow.shape[0]
flow_gray_b = np.zeros((b,1,flow.shape[2], flow.shape[3]))
for i in range(b):
flow_i = flow[i].asnumpy()
flow_im = flow_to_color(flow_i.transpose((1, 2, 0)))
flow_gray = cv2.cvtColor(flow_im, cv2.COLOR_RGB2GRAY)
flow_gray_b[i,:] = flow_gray
# flow = flow[0].detach().cpu().numpy()
# flow_im = flow_to_color(flow.transpose((1, 2, 0)))
# flow_gray = cv2.cvtColor(flow_im, cv2.COLOR_RGB2GRAY)
return flow_gray_b
def flow_color_1(flow):
flow = flow[0].asnumpy()
flow_im = flow_to_color(flow.transpose((1, 2, 0)))
flow_gray = cv2.cvtColor(flow_im, cv2.COLOR_RGB2GRAY)
return flow_gray
def save_flow(flow, rpath):#, graypath):
flow = flow[0].cpu().detach().numpy()
flow_im = flow_to_color(flow.transpose((1, 2, 0)))
cv2.imwrite(rpath, cv2.cvtColor(flow_im, cv2.COLOR_RGB2BGR))
# flow = flow[0].cpu().numpy()
# flow_im = flow_to_color(flow.transpose((1, 2, 0)))
# flow_rgb = cv2.cvtColor(flow_im, cv2.COLOR_RGB2BGR)
# cv2.imwrite(rpath, flow_rgb)
# flow_gray = cv2.cvtColor(flow_rgb, cv2.COLOR_BGR2GRAY)
# plt.imshow(flow_gray)
# plt.colorbar()
# plt.savefig(graypath)
# plt.clf()
def mask_gene(flow,sigma):
flow_gray = flow_color(flow)
base = O.ones((flow_gray.shape), mindspore.float32)
flow_gray = mindspore.Tensor(flow_gray)
for i in range(flow_gray.shape[0]):
base[i,:] *=O.reduce_mean(flow_gray,[1,2,3])[i]
mask = O.ones((flow_gray.shape), mindspore.float32)
mask[flow_gray<(1-sigma)*base] = 0
mask[flow_gray>(1+sigma)*base] = 0
return mask