def makeData(self):
w, h = model_wh('432x368')
e = TfPoseEstimator(get_graph_path('mobilenet_thin'), target_size=(w, h))
i = 0
cap = cv2.VideoCapture(WEBCAM_FILE_NAME)
while cap.isOpened():
endFlag, frame = cap.read()
if endFlag == False:
break
cv2.imwrite('./results/webcam/webcamRaw/'+'img_%s.png' % str(i).zfill(6), frame)
i += 1
j = 0
path_w = './results/webcam/webcamData.txt'
with open(path_w, mode='w') as f:
f.write('')
while j < i:
image = common.read_imgfile('./results/webcam/webcamRaw/'+'img_%s.png' % str(j).zfill(6))
humans = e.inference(image, resize_to_default=True, upsample_size=4.0)
centers = TfPoseEstimator.get_centers(image, humans, imgcopy=False)
with open(path_w, mode='a') as f:
f.write('t' + str(j) + ':' + str(centers) + '\n')
j += 1
k = 30
while k < 80:
image = common.read_imgfile('./results/webcam/webcamRaw/'+'img_%s.png' % str(k).zfill(6))
humans = e.inference(image, resize_to_default=True, upsample_size=4.0)
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
cv2.imwrite('./results/webcam2/webcamOpenpose/'+'img_%s.png' % str(k).zfill(6), image)
k += 1
num_12.append((x,y))
num_13 = []
for x, y in data["lower_body"]["13"]:
num_13.append((x,y))
index = 0
while cap.isOpened():
try:
ret_val, image = cam.read()
ret_val2, image2 = cap.read()
logger.debug('image process+')
humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)
logger.debug('postprocess+')
a = TfPoseEstimator.get_centers(image, humans, imgcopy=False)
b = []
b.append(num_1[index])
b.append(num_2[index])
b.append(num_3[index])
b.append(num_4[index])
b.append(num_5[index])
b.append(num_6[index])
b.append(num_7[index])
c = []
c.append(num_8[index])
c.append(num_9[index])
c.append(num_10[index])
c.append(num_11[index])
c.append(num_12[index])
c.append(num_13[index])
ret_val2, image2 = cap2.read()
logger1.debug('image process+')
humans1 = e1.inference(image1,
resize_to_default=(w1 > 0 and h1 > 0),
upsample_size=args1.resize_out_ratio)
logger2.debug('image process+')
humans2 = e2.inference(image2,
resize_to_default=(w2 > 0 and h2 > 0),
upsample_size=args2.resize_out_ratio)
### 2:Video) if(--showBG=False) print skeleton
if not args2.showBG:
image2 = np.zeros(image2.shape)
###
logger1.debug('postprocess+')
a = TfPoseEstimator.get_centers(image1, humans1,
imgcopy=False) #all points
image1 = TfPoseEstimator.draw_humans(image1,
humans1,
imgcopy=False)
logger2.debug('postprocess+')
b = TfPoseEstimator.get_centers(image2, humans2,
imgcopy=False) #상체 points
c = TfPoseEstimator.get_centers(image2, humans2,
imgcopy=False) #하체 points
image2 = TfPoseEstimator.draw_humans(image2,
humans2,
imgcopy=False)
"""
1) 실시간으로 동영상의 점을 불러온다 (점의 좌표를 알아야함)
2) 실시간으로 웹캠의 점을 불러온다 (점의 좌표를 알아야함)
3) 점 간의 norm(거리)을 구한다 (scalar)
logger2.debug('postprocess+')
image2 = TfPoseEstimator.draw_humans(image2,
humans2,
imgcopy=False)
"""
1) 실시간으로 동영상의 점을 불러온다 (점의 좌표를 알아야함)
2) 실시간으로 웹캠의 점을 불러온다 (점의 좌표를 알아야함)
3) 점 간의 norm(거리)을 구한다 (scalar)
4) 예를 들어 점이 18개로 고정되어 있다면 각 pair점 간의 norm을 전부 구하고
5) sum 하여 그 값을 0과 1사이로 normalization 한다 ->result
6) result를 y축 time을 x축으로 실시간 데이터 plotting
7) result가 어떤 threshold를 넘어설때 마다 warning을 cv2.putText로 출력해준다.
"""
# point 찾기
a = TfPoseEstimator.get_centers(image1, humans1, imgcopy=False)
b = TfPoseEstimator.get_centers(image2, humans2, imgcopy=False)
L2_norm = []
L2_nonzero = []
for i in range(len(b)):
try:
L2_norm.append(
np.linalg.norm(np.array(a[i]) - np.array(b[i]), ord=2))
except:
L2_norm.append(0.0)
pass
if L2_norm[i] is not 0.0:
L2_nonzero.append(L2_norm[i])
else:
pass
