OpenCV 的密集光流 (Farneback) 函数的输出是什么？这如何用于在 Python 中构建光流图？

Question

我正在尝试使用 Opencv 的密集光流函数的输出来绘制运动矢量的颤动图，但无法找到该函数实际输出的内容。代码如下：

import cv2
import numpy as np

cap = cv2.VideoCapture('GOPR1745.avi')

ret, frame1 = cap.read()
prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)

hsv[...,1] = 255
count=0

while(1):
    ret, frame2 = cap.read()
    next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)
    flow = cv2.calcOpticalFlowFarneback(prvs,next,None, 0.5, 3, 15, 3, 10, 1.2, 0)
    mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])

    hsv[...,0] = ang*180/np.pi/2
    hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
    rgb = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
    if count==10:
        count=0

        print "flow",flow

    cv2.imshow('frame2',rgb)
    count=count+1
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break
    elif k == ord('s'):
    prvs = next

cap.release()
cv2.destroyAllWindows()

这实际上与 OpenCv 密集光流教程中给出的代码相同。我从打印功能收到以下输出：

flow [[[  0.00000000e+00   0.00000000e+00]
  [  0.00000000e+00   0.00000000e+00]
  [  0.00000000e+00   0.00000000e+00]
  ..., 
  [  0.00000000e+00   0.00000000e+00]
  [  0.00000000e+00   0.00000000e+00]
  [  0.00000000e+00   0.00000000e+00]]

 ..., 
 [[ -3.54891084e-14  -1.38642463e-14]
  [ -2.58058853e-14  -1.54020863e-14]
  [ -5.56561768e-14  -1.88019359e-14]
  ..., 
  [ -7.59403916e-15   1.16633225e-13]
  [  7.22156371e-14  -1.61951507e-13]
  [ -4.30715618e-15  -4.39530987e-14]]

 [[ -3.54891084e-14  -1.38642463e-14]
  [ -2.58058853e-14  -1.54020863e-14]
  [ -5.56561768e-14  -1.88019359e-14]
  ..., 
  [ -7.59403916e-15   1.16633225e-13]
  [  7.22156371e-14  -1.61951507e-13]
  [ -4.30715618e-15  -4.39530987e-14]]

我想知道这些值到底是什么？原始 X,Y 坐标？最终的 X,Y 坐标？移动距离？

我计划尝试使用以下页面中的代码找到初始坐标和最终坐标以制作箭袋图： https://www.getdatajoy.com/examples/python-plots/vector-fields 这是因为在 python 中没有我知道的为你绘制光流图的函数。

提前谢谢你！

Answer 1

你快到了。让我们先看看上面写的calcOpticalFlowFarneback Documentation：

flow – 与prev 大小相同的计算流图并输入 CV_32FC2.

因此，您实际得到的是一个与输入帧大小相同的矩阵。
flow 矩阵中的每个元素都是一个点，表示该像素与prev 帧的位移。这意味着您会得到一个带有 x 和 y 值（以像素为单位）的点，它会为您提供最后一帧的 delta x 和 delta y。

Answer 2

我要劫持这个，因为它是同一个主题。

如果单位是@shravya 所述的像素，为什么这段代码不显示最大流量等于一？

我真的不明白单位

代码

import numpy as np
import cv2
import seaborn as sns

# Generating img
img = np.zeros(shape=(3,50,50)) # 3 time frames, 50x50

center0 = np.array((10,10))
center1 = np.array((30,30))
for each_time, each_x, each_y in itertools.product(range(img.shape[0]), range(img.shape[1]), range(img.shape[2])): 
    img[each_time, each_x, each_y] =  img[each_time, each_x, each_y] + 1000 *  1/(  0.1* ((center0[0]+each_time*displacement_x - each_x)**2 + 1*(center0[1]+each_time*displacement_y - each_y)**2)**0.5 + 1)    
    img[each_time, each_x, each_y] =  img[each_time, each_x, each_y] + 1000 * 1/(  0.1* ((center1[0]+each_time*displacement_x - each_x)**2 + 1*(center1[1]+each_time*displacement_y - each_y)**2)**0.5 + 1)


img = (img - img.min())/(img.max()-img.min()) # Normalizing

## Ploting
fig, axs = plt.subplots(ncols=3, squeeze=True, figsize=(20,5))
for i in range(3):
    im = sns.heatmap(img[i,:,:], ax = axs[i], vmin=0, vmax=np.max(img))

fig.suptitle('Image') 

def calc_flow(img):

    ## Optical flow
    img = img.astype(np.int16)

    prev = np.zeros(img[0, :, :].shape).astype(np.int16)
    flows = np.zeros(shape=(img.shape[0], img.shape[1], img.shape[2], 2))

    for i, each_frame in enumerate(img):
        if i > img.shape[0]:
            break

        next_ = each_frame
        flow = cv2.calcOpticalFlowFarneback(prev, next_, None,
                                           pyr_scale = 0.5,
                                            levels = 3,
                                            winsize = 12,
                                            iterations = 5,
                                            poly_n = 5,
                                            poly_sigma = 1.2,
                                            flags = 0) 

        flows[i, :, :, 0] = flow[..., 0]
        flows[i, :, :, 1] = flow[..., 1]

        prev = next_
    
    return flows

flow = calc_flow(img)

fig, axs = plt.subplots(ncols=3, nrows=2, squeeze=True, figsize=(20,10))
for i in range(3):
    im = sns.heatmap(flow[i,:,:, 0] ,ax = axs[0,i], vmin=0, vmax = np.max(flow)) 
    im = sns.heatmap(flow[i,:,:, 1] ,ax = axs[1,i], vmin=0, vmax = np.max(flow)) 
    
fig.suptitle('Flow x and y plots')

mag_img, pha_img = cv2.cartToPolar(flow[..., 0], flow[..., 1]) 

fig, axs = plt.subplots(ncols=3, nrows=2, squeeze=True, figsize=(20,10))
for i in range(3):
    im = sns.heatmap(mag_img[i,:,:], ax=axs[0,i], vmin=0, vmax = np.max(mag_img)) 
    im = sns.heatmap(pha_img[i,:,:], ax=axs[1,i], vmin=0, vmax = np.max(pha_img))  
    
fig.suptitle('Magnitude and phase plots')

## Output
print(flow.max()) # This should be equal to displacement!
print(np.abs(flow).min()) # this should be zero


print(mag_img.max()) # This should be equal to displacement!
print(mag_img.min()) # this should be zero