def his_image(im, size, joint, data):
joints = {
'lankle': 13,
'rankle': 10,
'lknee': 12,
'rknee': 9,
'lthi': 11,
'rthi': 8
}
# médian
#img_m=cv2.medianBlur(im,5)
#font=cv2.FONT_HERSHEY_SIMPLEX
img_m = im[0:828, 0:512]
'''
img_m_h=img_m[0:424, 0:512]
img_m_b=img_m[424:828, 0:512]
for i in range(424):
for j in range(512):
img_m[i,j]=img_m_h[i,j]*256/6+img_m_b[i,j]
'''
xjoints = data[0:18]
yjoints = data[18:]
x = xjoints[joints[joint]]
y = yjoints[joints[joint]]
(x_d, y_d) = tools.RGBtoD((x, y))
ndg_h1 = img_m[y_d, x_d][0] # Original Depth_high
ndg_b1 = img_m[y_d + 424, x_d][0] # Depth Original_low
ng = []
#print(img_m[(y_d-int((size-1)/2)):(y_d+int((size-1)/2)),(x_d-int((size-1)/2)):(x_d+int((size-1)/2))] )
for i in range(y_d - int((size - 1) / 2), y_d + int((size - 1) / 2) + 1):
for j in range(x_d - int((size - 1) / 2), x_d + int(
(size - 1) / 2) + 1):
z0 = (img_m[i, j] * 256 / 6 + img_m[i + 424, j])[0] / 10
if z0 > 120 and z0 < 750:
ng.append(int(round(z0)))
print(len(ng))
ngh = [0] * 750
for h in set(ng):
ngh[h] = ng.count(h)
zm = ngh.index(max(ngh)) * 10
print(zm)
return (zm, ndg_h1 * 256 / 6 + ndg_b1)
def his(nb_video, frame, size, joint):
files = os.listdir('testVedios/test' + nb_video + '/')
for i in files:
if (len(re.findall('.*C\.json', i)) != 0):
filejson = i
if (len(re.findall('.*D.mp4', i)) != 0):
filedepth = i
v = cv2.VideoCapture('testVedios/test' + nb_video + '/' + filedepth)
with open('testVedios/test' + nb_video + '/' + filejson) as json_data:
d = json.load(json_data)
#for t in range(frames[0],frames[1]):
#print(t)
v.set(cv2.CAP_PROP_POS_FRAMES, frame)
ret, im = v.read()
joints = {
'lankle': 1,
'rankle': 0,
'lknee': 3,
'rknee': 2,
'lthi': 5,
'rthi': 4
}
# médian
img_m = cv2.medianBlur(im, 5)
font = cv2.FONT_HERSHEY_SIMPLEX
img_m = img_m[0:828, 0:512]
ng = []
xjoints = d['frames'][frame][0]['pose2d'][:13]
yjoints = d['frames'][frame][0]['pose2d'][13:]
x = xjoints[joints[joint]]
y = yjoints[joints[joint]]
(x_d, y_d) = tools.RGBtoD((x, y), frame)
ndg_h1 = img_m[y_d, x_d][0] # Original Depth_high
ndg_b1 = img_m[y_d + 424, x_d][0] # Depth Original_low
#print(img_m[(y_d-int((size-1)/2)):(y_d+int((size-1)/2)),(x_d-int((size-1)/2)):(x_d+int((size-1)/2))] )
for i in range(y_d - int((size - 1) / 2), y_d + int((size - 1) / 2) + 1):
for j in range(x_d - int((size - 1) / 2), x_d + int(
(size - 1) / 2) + 1):
z0 = (img_m[i, j] * 256 / 6 + img_m[i + 424, j])[0] / 10
if z0 > 120 and z0 < 750:
ng.append(int(round(z0)))
print(len(ng))
print(ng)
ngh = [0] * 750
for h in set(ng):
ngh[h] = ng.count(h)
zm = ngh.index(max(ngh)) * 10
print('max: ', zm)
print('original: ', ndg_h1 * 256 / 6 + ndg_b1)
# mask first 8 bits (high)
mask = np.zeros(img_m.shape[:2], np.uint8)
mask[(y_d - int((size - 1) / 2)):(y_d + int((size - 1) / 2)) + 1,
(x_d - int((size - 1) / 2)):(x_d + int((size - 1) / 2)) + 1] = 255
masked_img = cv2.bitwise_and(img_m, img_m, mask=mask)
# mask_bas last 8 bits (low)
mask_bas = np.zeros(img_m.shape[:2], np.uint8)
mask_bas[(y_d + 424 -
int((size - 1) / 2)):(y_d + 424 + int((size - 1) / 2)) + 1,
(x_d - int((size - 1) / 2)):(x_d + int((size - 1) / 2) + 1)] = 255
masked_bas_img = cv2.bitwise_and(img_m, img_m, mask=mask_bas)
#opencv方法读取-cv2.calcHist（速度最快）
#图像，通道[0]-灰度图，掩膜-无，灰度级，像素范围
hist_bas = cv2.calcHist([img_m], [0], mask_bas, [256], [0, 256]) #11178
hist_mask = cv2.calcHist([img_m], [0], mask, [256], [0, 256])
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax1.imshow(img_m, 'gray')
ax1.set_title('Depth map')
ax1.scatter(x_d, y_d, s=1, color='red')
ax1.plot([
x_d - size / 2, x_d - size / 2, x_d + size / 2, x_d + size / 2,
x_d - size / 2
], [
y_d - size / 2, y_d + size / 2, y_d + size / 2, y_d - size / 2,
y_d - size / 2
],
color='orange')
ax1.scatter(x_d, y_d + 424, s=3, color='red')
ax1.plot([
x_d - size / 2, x_d - size / 2, x_d + size / 2, x_d + size / 2,
x_d - size / 2
], [
y_d + 424 - size / 2, y_d + 424 + size / 2, y_d + 424 + size / 2,
y_d + 424 - size / 2, y_d + 424 - size / 2
],
color='orange')
ax2 = fig.add_subplot(222)
ax2.set_title('Histogramme')
ax2.set_xlabel('Distance(cm)')
#ax2.imshow(mask_bas,'gray')
ax2.hist(np.array(ng), bins=size**2 + 1)
#ax2.scatter(ndg_b1*256/6+ndg_b1, 15, color='orange')
# low
ax3 = fig.add_subplot(223)
ax3.set_title('Histogramme bas')
ax3.plot(hist_bas)
#print (hist_bas[signal.argrelextrema(hist_bas, np.greater)])# signal.argrelextrema(hist_mask, np.greater) jizhidexiabiao
#print (signal.argrelextrema(hist_bas, np.greater) )
ax3.plot(
signal.argrelextrema(hist_bas, np.greater)[0],
hist_bas[signal.argrelextrema(hist_bas, np.greater)], 'o')
ax3.plot(
signal.argrelextrema(-hist_bas, np.greater)[0],
hist_bas[signal.argrelextrema(-hist_bas, np.greater)], '+')
ndg_b = ndg_b1
number = hist_bas[ndg_b]
for i in signal.argrelextrema(hist_bas, np.greater)[0]:
#print(i, hist_bas[i])
if (hist_bas[i] >= number):
number = hist_bas[i]
ndg_b = i
ax3.scatter(ndg_b1, hist_bas[ndg_b1], color='black', s=50)
#print('max: ', ndg_b, hist_bas[ndg_b])
# hight
ax4 = fig.add_subplot(224)
ax4.plot(hist_mask)
ax4.set_title('Histogramme haut')
#print (hist_mask[signal.argrelextrema(hist_mask, np.greater)])# signal.argrelextrema(hist_mask, np.greater) jizhidexiabiao
#print (signal.argrelextrema(hist_mask, np.greater) )
ax4.plot(
signal.argrelextrema(hist_mask, np.greater)[0],
hist_mask[signal.argrelextrema(hist_mask, np.greater)[0]], 'o')
ax4.plot(
signal.argrelextrema(-hist_mask, np.greater)[0],
hist_mask[signal.argrelextrema(-hist_mask, np.greater)[0]], '+')
ndg_h = ndg_h1
number = hist_mask[ndg_h]
for i in signal.argrelextrema(hist_mask, np.greater)[0]:
#print(i, hist_mask[i])
if (hist_mask[i] >= number and ((i >= 35) and i < 155)):
number = hist_mask[i]
ndg_h = i
ax4.scatter(ndg_h1, hist_mask[ndg_h1], color='black', s=50)
#print('max: ', ndg_h, hist_mask[ndg_h])
plt.show()
return (zm, ndg_h1 * 256 / 6 + ndg_b1)
def show_angle3d(nb_video, frames):
b = str(frames[0])
if frames[0] <= 200:
direction = 'Front'
else:
direction = 'Back'
files = os.listdir('testVideos/test' + nb_video + '/')
for i in files:
if (len(re.findall('.*C\.json', i)) != 0):
filejson = i
if (len(re.findall('.*D.mp4', i)) != 0):
filedepth = i
if (len(re.findall('.*C.mp4', i)) != 0):
filecolor = i
if (len(
re.findall(
'_GT-Vicon_PifPaf_3DKinect_' + direction + '_generalTF',
i)) != 0):
filegtfm = i
if (len(
re.findall(
'_GT-Vicon_PifPaf_3DKinect_' + direction + '_respectiveTF',
i)) != 0):
filegtf = i
if (len(
re.findall(
'_GT-Vicon_PifPaf_2DColor_' + direction + '_generalTF', i))
!= 0):
filegtfcm = i
if (len(
re.findall(
'_GT-Vicon_PifPaf_2DColor_' + direction + '_respectiveTF',
i)) != 0):
filegtfc = i
if (len(re.findall('_PifPaf_joints_3DKinect_front.csv', i)) != 0):
filekinect = i
print(filejson)
print(filedepth)
print(filecolor)
print(filekinect)
fig = plt.figure()
fig2 = plt.figure()
fig3 = plt.figure()
datagtm = pd.DataFrame(
pd.read_csv('testVideos/test' + nb_video + '/' + filegtfm))
datagt = pd.DataFrame(
pd.read_csv('testVideos/test' + nb_video + '/' + filegtf))
datagtcm = pd.DataFrame(
pd.read_csv('testVideos/test' + nb_video + '/' + filegtfcm))
datagtc = pd.DataFrame(
pd.read_csv('testVideos/test' + nb_video + '/' + filegtfc))
datakin = pd.DataFrame(
pd.read_csv('testVideos/test' + nb_video + '/' + filekinect))
def call_back(event):
axtemp = event.inaxes
x_min, x_max = axtemp.get_xlim()
y_min, y_max = axtemp.get_ylim()
z_min, z_max = axtemp.get_zlim()
dx = (x_max - x_min) / 10
dy = (y_max - y_min) / 10
dz = (z_max - z_min) / 10
if event.button == 'up':
axtemp.set(xlim=(x_min + dx, x_max - dx))
axtemp.set(ylim=(y_min + dy, y_max - dy))
axtemp.set(zlim=(z_min + dz, z_max - dz))
elif event.button == 'down':
axtemp.set(xlim=(x_min - dx, x_max + dx))
axtemp.set(ylim=(y_min - dy, y_max + dy))
axtemp.set(zlim=(z_min - dz, z_max + dz))
fig2.canvas.draw_idle() # 绘图动作实时反映在图像上
fig2.canvas.mpl_connect('scroll_event', call_back)
fig2.canvas.mpl_connect('button_press_event', call_back)
# Depthmap
ax = fig2.add_subplot(111, projection='3d')
ax.set_title('PifPaf 2D to Kinect 3D_' + b)
# Color Image
ax2 = fig.add_subplot(111)
# fig=plt.figure()
ax2.set_title('PifPaf 2D in Color image_' + b)
ax5 = fig3.add_subplot(111)
ax5.set_title('PifPaf 2D in Depth image_' + b)
# sk2d in depthmap
#ax4=fig.add_subplot(223, projection='3d')
#ax4.set_title('LCR-NET SK 2D in Depth map_'+b+'m front')
v_c = cv2.VideoCapture('testVideos/test' + nb_video + '/' + filecolor)
with open('testVideos/test' + nb_video + '/' + filejson) as json_data:
d = json.load(json_data)
v = cv2.VideoCapture('testVideos/test' + nb_video + '/' + filedepth)
anglesl = []
anglesr = []
for t in range(frames[0], frames[1]):
#print(t)
v.set(cv2.CAP_PROP_POS_FRAMES, t)
ret, im = v.read()
v_c.set(cv2.CAP_PROP_POS_FRAMES, t)
retc, im_c = v_c.read()
# médian
img_m = cv2.medianBlur(im, 5)
font = cv2.FONT_HERSHEY_SIMPLEX
x = []
y = []
z = []
# show 3d claud points
for i in range(600, 1000, 3):
for j in range(200, 960, 6):
a = tools.RGBto3D((i, j), img_m, t)
x.append(a[0])
y.append(a[1])
z.append(a[2])
ax.scatter(x, z, y, s=1)
# Show GT
if int(datagt['frame'][0]) <= t and int(
datagt['frame'][len(datagt['frame']) - 1] >= t):
xg = []
yg = []
zg = []
xgm = []
ygm = []
zgm = []
for i in datagt.keys():
if i.find('x') != -1:
print((datagt[i][datagt['frame'] == t]))
xg.append(float(datagt[i][datagt['frame'] == t]))
xgm.append(float(datagtm[i][datagtm['frame'] == t]))
#xg.append(datagt[i][])
#xg.append(datagt[i][datagt.frame==t])
if i.find('y') != -1:
yg.append(float(datagt[i][datagt['frame'] == t]))
ygm.append(float(datagtm[i][datagtm['frame'] == t]))
if i.find('z') != -1:
zg.append(float(datagt[i][datagt['frame'] == t]))
zgm.append(float(datagtm[i][datagtm['frame'] == t]))
print(xg, yg, zg)
# GT joints 3D
ax.scatter(xg, zg, yg, color='black')
ax.scatter(xgm, zgm, ygm, color='y')
#GT SK 3D
ax.plot([xg[k] for k in [0, 2, 4, 5, 3, 1]],
[zg[k] for k in [0, 2, 4, 5, 3, 1]],
[yg[k] for k in [0, 2, 4, 5, 3, 1]],
color='black')
ax.plot([xg[k] for k in [6, 8, 10, 11, 9, 7]],
[zg[k] for k in [6, 8, 10, 11, 9, 7]],
[yg[k] for k in [6, 8, 10, 11, 9, 7]],
color='black')
ax.plot([(xg[10] + xg[11]) / 2,
(xg[4] + xg[5]) / 2], [(zg[10] + zg[11]) / 2,
(zg[4] + zg[5]) / 2],
[(yg[10] + yg[11]) / 2, (yg[4] + yg[5]) / 2],
color='black')
ax.plot([xgm[k] for k in [0, 2, 4, 5, 3, 1]],
[zgm[k] for k in [0, 2, 4, 5, 3, 1]],
[ygm[k] for k in [0, 2, 4, 5, 3, 1]],
color='y')
ax.plot([xgm[k] for k in [6, 8, 10, 11, 9, 7]],
[zgm[k] for k in [6, 8, 10, 11, 9, 7]],
[ygm[k] for k in [6, 8, 10, 11, 9, 7]],
color='y')
ax.plot([(xgm[10] + xgm[11]) / 2,
(xgm[4] + xgm[5]) / 2], [(zgm[10] + zgm[11]) / 2,
(zgm[4] + zgm[5]) / 2],
[(ygm[10] + ygm[11]) / 2, (ygm[4] + ygm[5]) / 2],
color='y')
# Show GT 2D
xgc = []
ygc = []
xgcm = []
ygcm = []
for i in datagtc.keys():
if i.find('x') != -1:
print((datagtc[i][datagtc['frame'] == t]))
xgc.append(float(datagtc[i][datagtc['frame'] == t]))
xgcm.append(float(datagtcm[i][datagtcm['frame'] == t]))
#xg.append(datagt[i][])
#xg.append(datagt[i][datagt.frame==t])
if i.find('y') != -1:
ygc.append(float(datagtc[i][datagtc['frame'] == t]))
ygcm.append(float(datagtcm[i][datagtcm['frame'] == t]))
print(xgc, ygc)
# GT joints 2D color
ax2.scatter(xgc, ygc, color='black')
ax2.scatter(xgcm, ygcm, color='y')
#GT SK 2D color
ax2.plot([xgc[k] for k in [0, 2, 4, 5, 3, 1]],
[ygc[k] for k in [0, 2, 4, 5, 3, 1]],
color='black')
ax2.plot([xgc[k] for k in [6, 8, 10, 11, 9, 7]],
[ygc[k] for k in [6, 8, 10, 11, 9, 7]],
color='black')
ax2.plot([(xgc[10] + xgc[11]) / 2, (xgc[4] + xgc[5]) / 2],
[(ygc[10] + ygc[11]) / 2, (ygc[4] + ygc[5]) / 2],
color='black')
ax2.plot([xgcm[k] for k in [0, 2, 4, 5, 3, 1]],
[ygcm[k] for k in [0, 2, 4, 5, 3, 1]],
color='y')
ax2.plot([xgcm[k] for k in [6, 8, 10, 11, 9, 7]],
[ygcm[k] for k in [6, 8, 10, 11, 9, 7]],
color='y')
ax2.plot([(xgcm[10] + xgcm[11]) / 2, (xgcm[4] + xgcm[5]) / 2],
[(ygcm[10] + ygcm[11]) / 2, (ygcm[4] + ygcm[5]) / 2],
color='y')
# Show corrected Kinect 3D
if int(datakin['frames'][0]) <= t and int(
datakin['frames'][len(datakin['frames']) - 1] >= t):
xk = []
yk = []
zk = []
k = 0
for i in datakin.keys():
if i.find('x') != -1:
print((datakin[i][datakin['frames'] == t]))
xk.append(float(datakin[i][datakin['frames'] == t]))
if i.find('y_') != -1:
yk.append(float(datakin[i][datakin['frames'] == t]))
if i.find('z') != -1:
zk.append(float(datakin[i][datakin['frames'] == t]))
print('Corrected kinect: ', len(xk), len(yk), len(zk))
#corrected joints 3D
ax.scatter(xk, zk, yk, color='#800080')
#corrected SK 3D
ax.plot([xk[k] for k in [10, 8, 6, 5, 7, 9]],
[zk[k] for k in [10, 8, 6, 5, 7, 9]],
[yk[k] for k in [10, 8, 6, 5, 7, 9]],
color='#800080')
ax.plot([xk[k] for k in [16, 14, 12, 11, 13, 15]],
[zk[k] for k in [16, 14, 12, 11, 13, 15]],
[yk[k] for k in [16, 14, 12, 11, 13, 15]],
color='#800080')
ax.plot([xk[0], (xk[5] + xk[6]) / 2, (xk[11] + xk[12]) / 2],
[zk[0], (zk[5] + zk[6]) / 2, (zk[11] + zk[12]) / 2],
[yk[0], (yk[5] + yk[6]) / 2, (yk[11] + yk[12]) / 2],
color='#800080')
if len(d['frames'][t]) != 0:
#SK 2D
xjoints = d['frames'][t][0]['pose2d'][:17]
yjoints = d['frames'][t][0]['pose2d'][17:]
#SK 2D in Depth
xjoints_3d = []
yjoints_3d = []
zjoints_3d = []
for i in range(17):
joints = tools.RGBto3D((xjoints[i], yjoints[i]), img_m, t,
True, 7)
xjoints_3d.append(joints[0])
yjoints_3d.append(joints[1])
zjoints_3d.append(joints[2])
print(xjoints_3d, yjoints_3d, zjoints_3d)
# show joints
ax.scatter(xjoints_3d, zjoints_3d, yjoints_3d, color='r')
#ax4.scatter(xjoints_3d, zjoints_3d, yjoints_3d , color='r')
'''
anglesl.append(180-tools.angle((xjoints_3d[1],yjoints_3d[1],zjoints_3d[1]),(xjoints_3d[3],yjoints_3d[3],zjoints_3d[3]),(xjoints_3d[5],yjoints_3d[5],zjoints_3d[5])))
anglesr.append(180-tools.angle((xjoints_3d[0],yjoints_3d[0],zjoints_3d[0]),(xjoints_3d[2],yjoints_3d[2],zjoints_3d[2]),(xjoints_3d[4],yjoints_3d[4],zjoints_3d[4])))
gt=(54.115166+1100, -1168.320923+27124, 484.712952+1100)
#ax.scatter(gt[0],gt[1],gt[2], color='y')
print(xjoints_3d[3], zjoints_3d[3], yjoints_3d[3])
print(anglesl)
print(anglesr)
'''
# ax show 3d skelecton
ax.plot([xjoints_3d[k] for k in [10, 8, 6, 5, 7, 9]],
[zjoints_3d[k] for k in [10, 8, 6, 5, 7, 9]],
[yjoints_3d[k] for k in [10, 8, 6, 5, 7, 9]],
color='#C71585')
ax.plot([xjoints_3d[k] for k in [16, 14, 12, 11, 13, 15]],
[zjoints_3d[k] for k in [16, 14, 12, 11, 13, 15]],
[yjoints_3d[k] for k in [16, 14, 12, 11, 13, 15]],
color='#C71585')
ax.plot([
xjoints_3d[0], (xjoints_3d[5] + xjoints_3d[6]) / 2,
(xjoints_3d[11] + xjoints_3d[12]) / 2
], [
zjoints_3d[0], (zjoints_3d[5] + zjoints_3d[6]) / 2,
(zjoints_3d[11] + zjoints_3d[12]) / 2
], [
yjoints_3d[0], (yjoints_3d[5] + yjoints_3d[6]) / 2,
(yjoints_3d[11] + yjoints_3d[12]) / 2
],
color='#C71585')
# ax4 show 3d skelecton
#ax4.plot([xjoints_3d[k] for k in [0,2,4,5,3,1]], [zjoints_3d[k] for k in [0,2,4,5,3,1]], [yjoints_3d[k] for k in [0,2,4,5,3,1]], color='orange' )
#ax4.plot([xjoints_3d[k] for k in [6,8,10,11,9,7]], [zjoints_3d[k] for k in [6,8,10,11,9,7]], [yjoints_3d[k] for k in [6,8,10,11,9,7]], color='orange' )
#ax4.plot([xjoints_3d[12] , (xjoints_3d[10]+xjoints_3d[11])/2 ,(xjoints_3d[4]+xjoints_3d[5])/2], [zjoints_3d[12], (zjoints_3d[10]+zjoints_3d[11])/2, (zjoints_3d[4]+zjoints_3d[5])/2], [yjoints_3d[12], (yjoints_3d[10]+yjoints_3d[11])/2, (yjoints_3d[4]+yjoints_3d[5])/2], color='orange' )
# show depth image and the joints
ax5.imshow(im)
xjoints_d = [
tools.RGBtoD((xjoints[i], yjoints[i]), t)[0] for i in range(17)
]
yjoints_d = [
tools.RGBtoD((xjoints[i], yjoints[i]), t)[1] for i in range(17)
]
ax5.plot([xjoints_d[k] for k in [10, 8, 6, 5, 7, 9]],
[yjoints_d[k] for k in [10, 8, 6, 5, 7, 9]],
color='#C71585')
ax5.plot([xjoints_d[k] for k in [16, 14, 12, 11, 13, 15]],
[yjoints_d[k] for k in [16, 14, 12, 11, 13, 15]],
color='#C71585')
ax5.plot([
xjoints_d[0], (xjoints_d[5] + xjoints_d[6]) / 2,
(xjoints_d[11] + xjoints_d[12]) / 2
], [
yjoints_d[0], (yjoints_d[5] + yjoints_d[6]) / 2,
(yjoints_d[11] + yjoints_d[12]) / 2
],
color='#C71585')
ax5.scatter(xjoints_d, yjoints_d, color='r', s=5)
# show color image and the joints with 3d skelecton
ax2.imshow(im_c)
ax2.plot([xjoints[k] for k in [10, 8, 6, 5, 7, 9]],
[yjoints[k] for k in [10, 8, 6, 5, 7, 9]],
color='#C71585')
ax2.plot([xjoints[k] for k in [16, 14, 12, 11, 13, 15]],
[yjoints[k] for k in [16, 14, 12, 11, 13, 15]],
color='#C71585')
ax2.plot([
xjoints[0], (xjoints[5] + xjoints[6]) / 2,
(xjoints[11] + xjoints[12]) / 2
], [
yjoints[0], (yjoints[5] + yjoints[6]) / 2,
(yjoints[11] + yjoints[12]) / 2
],
color='#C71585')
ax2.scatter(xjoints, yjoints, color='r', s=5)
# show the joints' positions
'''
for a in [6,12,14,16,8]:
ax2.text(xjoints[a], yjoints[a], '(%.1f, %.1f, %.1f)'%(xjoints_3d[a],yjoints_3d[a], zjoints_3d[a])+'point'+str(a), color='blue')
for a in [5,7,11,13,15]:
ax2.text(xjoints[a], yjoints[a], '(%.1f, %.1f, %.1f)'%(xjoints_3d[a],yjoints_3d[a], zjoints_3d[a])+'point'+str(a), color='orange')
ax2.text(xjoints[0], yjoints[0], '(%.1f, %.1f, %.1f)'%(xjoints_3d[0],yjoints_3d[0], zjoints_3d[0])+'point'+str(0), color='g')
ax2.text(xjoints[10], yjoints[10], '(%.1f, %.1f, %.1f)'%(xjoints_3d[10],yjoints_3d[10], zjoints_3d[10])+'point'+str(10), color='g')
ax2.text(xjoints[9], yjoints[9], '(%.1f, %.1f, %.1f)'%(xjoints_3d[9],yjoints_3d[9], zjoints_3d[9])+'point'+str(9), color='g')
'''
for a in [6, 12, 14, 16, 8]:
ax2.text(xjoints[a] - 35,
yjoints[a],
str(a).zfill(2),
color='blue')
for a in [5, 7, 11, 13, 15]:
ax2.text(xjoints[a], yjoints[a], str(a).zfill(2), color='orange')
ax2.text(xjoints[0] + 10, yjoints[0] - 10, str(0).zfill(2), color='g')
ax2.text(xjoints[10] - 35, yjoints[10], str(10).zfill(2), color='blue')
ax2.text(xjoints[9], yjoints[9], str(9).zfill(2), color='orange')
'''
print(xjoints)
print(yjoints)
print(zjoints)
ax2.scatter(xjoints, zjoints, yjoints, color='r')
'''
v.release()
frames = range(frames[0], frames[1])
#ax.plot(frames, anglesl)
#ax2.plot(frames, anglesr)
plt.show()
def his(nb_video, frame, size, joint):
files = os.listdir('testVideos/test' + nb_video + '/')
for i in files:
if (len(re.findall('.*C\.json', i)) != 0):
filejson = i
if (len(re.findall('.*D.mp4', i)) != 0):
filedepth = i
v = cv2.VideoCapture('testVideos/test' + nb_video + '/' + filedepth)
with open('testVideos/test' + nb_video + '/' + filejson) as json_data:
d = json.load(json_data)
#for t in range(frames[0],frames[1]):
#print(t)
v.set(cv2.CAP_PROP_POS_FRAMES, frame)
ret, im = v.read()
joints = {
'lankle': 13,
'rankle': 10,
'lknee': 12,
'rknee': 9,
'lthi': 11,
'rthi': 8
}
# médian
img_m = cv2.medianBlur(im, 5)
font = cv2.FONT_HERSHEY_SIMPLEX
img_m = img_m[0:828, 0:512]
ng = []
xjoints = d['frames'][frame][0]['pose2d'][:18]
yjoints = d['frames'][frame][0]['pose2d'][18:]
x = xjoints[joints[joint]]
y = yjoints[joints[joint]]
(x_d, y_d) = tools.RGBtoD((x, y))
ndg_h1 = img_m[y_d, x_d][0] # Original Depth_high
ndg_b1 = img_m[y_d + 424, x_d][0] # Depth Original_low
#print(img_m[(y_d-int((size-1)/2)):(y_d+int((size-1)/2)),(x_d-int((size-1)/2)):(x_d+int((size-1)/2))] )
for i in range(y_d - int((size - 1) / 2), y_d + int((size - 1) / 2) + 1):
for j in range(x_d - int((size - 1) / 2), x_d + int(
(size - 1) / 2) + 1):
z0 = (img_m[i, j] * 256 / 6 + img_m[i + 424, j])[0] / 10
if z0 > 200 and z0 < 700:
ng.append(int(round(z0)) - 200)
print(len(ng))
img_hist = np.zeros((1, 500), np.uint8)
for h in set(ng):
img_hist[0, h] = ng.count(h)
maxi = np.max(img_hist)
mini = np.min(img_hist)
for im in range(500):
img_hist[0, im] = round((img_hist[0, im] - mini) / (maxi - mini) * 255)
#print(img_hist)
# mask first 8 bits (high)
mask = np.zeros(img_m.shape[:2], np.uint8)
mask[(y_d - int((size - 1) / 2)):(y_d + int((size - 1) / 2)) + 1,
(x_d - int((size - 1) / 2)):(x_d + int((size - 1) / 2)) + 1] = 255
masked_img = cv2.bitwise_and(img_m, img_m, mask=mask)
# mask_bas last 8 bits (low)
mask_bas = np.zeros(img_m.shape[:2], np.uint8)
mask_bas[(y_d + 424 -
int((size - 1) / 2)):(y_d + 424 + int((size - 1) / 2)) + 1,
(x_d - int((size - 1) / 2)):(x_d + int((size - 1) / 2) + 1)] = 255
masked_bas_img = cv2.bitwise_and(img_m, img_m, mask=mask_bas)
#opencv方法读取-cv2.calcHist（速度最快）
#图像，通道[0]-灰度图，掩膜-无，灰度级，像素范围
hist_bas = cv2.calcHist([img_m], [0], mask_bas, [256], [0, 256]) #11178
hist_mask = cv2.calcHist([img_m], [0], mask, [256], [0, 256])
'''
ndg_b=ndg_b1
number=hist_bas[ndg_b]
for i in signal.argrelextrema(hist_bas, np.greater)[0]:
#print(i, hist_bas[i])
if(hist_bas[i]>=number):
number=hist_bas[i]
ndg_b=i
ndg_h=ndg_h1
number=hist_mask[ndg_h]
for i in signal.argrelextrema(hist_mask, np.greater)[0]:
#print(i, hist_mask[i])
if(hist_mask[i]>=number and ((i>=35) and i<155)):
number=hist_mask[i]
ndg_h=i
'''
return (ndg_h1, ndg_b1, ndg_h1 * 256 / 6 + ndg_b1, img_hist)
def show_angle3d(nb_vedio, frames):
files = os.listdir('testVedios/test' + nb_vedio + '/')
for i in files:
if (len(re.findall('.*C\.json', i)) != 0):
filejson = i
if (len(re.findall('.*D.mp4', i)) != 0):
filedepth = i
number = filedepth[:-5]
#if (len(re.findall('_distance_data.csv', i))!=0):
# filedis=i
print(number)
#print(filedis)
print(filejson)
#disdata=pd.DataFrame(pd.read_csv('testVedios/test'+nb_vedio+'/'+filedis))
fig = plt.figure()
ax = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax.set_title('3d kinect LCR-NET_Improved')
ax.set_xlabel('Frame')
ax.set_ylabel('Angle_left')
ax2.set_xlabel('Frame')
ax2.set_ylabel('Angle_right')
with open('testVedios/test' + nb_vedio + '/' + filejson) as json_data:
d = json.load(json_data)
v = cv2.VideoCapture('testVedios/test' + nb_vedio + '/' + filedepth)
anglesl = []
anglesr = []
points3dx = [[] for p in range(13)]
points3dy = [[] for p in range(13)]
points3dz = [[] for p in range(13)]
points2dx = [[] for p in range(13)]
points2dy = [[] for p in range(13)]
points2ddx = [[] for p in range(13)]
points2ddy = [[] for p in range(13)]
akdis = []
kndis = []
step = []
bad_frame = [0] * (frames[1] - frames[0])
l_kn_ak = []
r_kn_ak = []
for t in tqdm(range(frames[0], frames[1])):
#print(t)
v.set(cv2.CAP_PROP_POS_FRAMES, t)
ret, im = v.read()
img_m = cv2.medianBlur(im, 7)
font = cv2.FONT_HERSHEY_SIMPLEX
'''
for i in range(800,980,2):
for j in range(300,860,5):
a=RGBto3D((i,j),im)
x.append(a[0])
y.append(a[1])
z.append(a[2])
print(i)
#ax.scatter(x,z,y)
'''
if len(d['frames'][t]) != 0:
xjoints2d = d['frames'][t][0]['pose2d'][:13]
yjoints2d = d['frames'][t][0]['pose2d'][13:]
xjoints3d = []
yjoints3d = []
zjoints3d = []
xjoints2dd = []
yjoints2dd = []
###!!!!!!!!!11111!!!!
leg = ['z_rak', 'z_lak', 'z_rkn', 'z_lkn', 'z_ras', 'z_las']
for i in range(13):
if i <= -5:
z3d = float(disdata[leg[i]][disdata['frame'] == t])
print(z3d)
joints = tools.RGBto3D((xjoints2d[i], yjoints2d[i], z3d),
im, t)
xjoints3d.append(joints[0])
yjoints3d.append(joints[1])
zjoints3d.append(joints[2])
else:
joints = tools.RGBto3D((xjoints2d[i], yjoints2d[i]), im, t,
True, 7)
xjoints3d.append(joints[0])
yjoints3d.append(joints[1])
zjoints3d.append(joints[2])
joints2dd = tools.RGBtoD((xjoints2d[i], yjoints2d[i]), t)
xjoints2dd.append(joints2dd[0])
yjoints2dd.append(joints2dd[1])
for p in range(13):
points3dx[p].append(xjoints3d[p])
points3dy[p].append(yjoints3d[p])
points3dz[p].append(zjoints3d[p])
points2dx[p].append(xjoints2d[p])
points2dy[p].append(yjoints2d[p])
points2ddx[p].append(xjoints2dd[p])
points2ddy[p].append(yjoints2dd[p])
anglesl.append(180 -
tools.angle((xjoints3d[1], yjoints3d[1], zjoints3d[1]),
(xjoints3d[3], yjoints3d[3],
zjoints3d[3]), (xjoints3d[5], yjoints3d[5],
zjoints3d[5]), False))
anglesr.append(180 -
tools.angle((xjoints3d[0], yjoints3d[0], zjoints3d[0]),
(xjoints3d[2], yjoints3d[2],
zjoints3d[2]), (xjoints3d[4], yjoints3d[4],
zjoints3d[4]), False))
# static analysis
# save the important information:dis between ankles, as height, leg length
l_kn_ak.append(
tools.get_distance((xjoints3d[1], yjoints3d[1], zjoints3d[1]),
(xjoints3d[3], yjoints3d[3], zjoints3d[3])))
r_kn_ak.append(
tools.get_distance((xjoints3d[0], yjoints3d[0], zjoints3d[0]),
(xjoints3d[2], yjoints3d[2], zjoints3d[2])))
dak = (zjoints3d[0] - zjoints3d[1])
dkn = (zjoints3d[2] - zjoints3d[3])
akdis.append(dak)
kndis.append(dkn)
# dynamic analysis
# gen qian mian de zhen xiang bi jiao
# Model : step automatic by Vicon !!!!front back!!!!! condition 1 2 3
# dis>0 : left joint is in front, dis<0 : right joint is in front
if dkn <= 0 and dak > -100:
step.append(0)
elif dak <= -100 and dkn < 0:
step.append(1)
elif dkn >= 0 and dak < 100:
step.append(2)
elif dak >= 100 and dkn > 0:
step.append(3)
else:
step.append(-1)
# find the wrong frame
# +++++++++++
# correct the wrong ponts
# +++++++++++
# angle 2d
# anglesl.append(180-tools.angle((yjoints[1],zjoints[1]),(yjoints[3],zjoints[3]),(yjoints[5],zjoints[5])))
# anglesr.append(180-tools.angle((yjoints[0],zjoints[0]),(yjoints[2],zjoints[2]),(yjoints[4],zjoints[4])))
'''
print(xjoints)
print(yjoints)
print(zjoints)
ax2.scatter(xjoints, zjoints, yjoints, color='r')
'''
v.release()
#b, a = signal.butter(8, 0.3, 'lowpass')
#anglesl = signal.filtfilt(b, a, anglesl)
#anglesr = signal.filtfilt(b, a, anglesr)
frames = range(frames[0], frames[1])
ax.plot(frames, anglesl)
ax2.plot(frames, anglesr, color='r') #, marker='.')
#for i in range(50): y1.append(i) # 每迭代一次，将i放入y1中画出来 ax.cla() # 清除键 ax.bar(y1, label='test', height=y1, width=0.3) ax.legend() plt.pause(0.1)
data2d = {}
data = {}
data2dd = {}
data2dd['frames'] = frames
data2d['frames'] = frames
data['frames'] = frames
data['kangle_l'] = anglesl
data['kangle_r'] = anglesr
data['akdis'] = akdis
data['kndis'] = kndis
data['l_kn_ak'] = l_kn_ak
data['r_kn_ak'] = r_kn_ak
data['bad_frame'] = bad_frame
print(len(step))
data['step'] = step
j = [
'rak', 'lak', 'rkn', 'lkn', 'ras', 'las', 'rwr', 'lwr', 'rel', 'lel',
'rsh', 'lsh', 'head'
]
for p in range(13):
data['x_' + j[p]] = points3dx[p]
data['y_' + j[p]] = points3dy[p]
data['z_' + j[p]] = points3dz[p]
data2d['x_' + j[p]] = points2dx[p]
data2d['y_' + j[p]] = points2dy[p]
data2dd['x_' + j[p]] = points2ddx[p]
data2dd['y_' + j[p]] = points2ddy[p]
data = pd.DataFrame(data)
data2d = pd.DataFrame(data2d)
data2dd = pd.DataFrame(data2dd)
if frames[0] > 250:
data.to_csv('testVedios' + '/test' + nb_vedio + '/' + number +
'_LCR-NET_joints_3DKinect_back.csv',
encoding='gbk')
data2d.to_csv('testVedios' + '/test' + nb_vedio + '/' + number +
'_LCR-NET_joints_2DColor_back.csv',
encoding='gbk')
data2dd.to_csv('testVedios' + '/test' + nb_vedio + '/' + number +
'_LCR-NET_joints_2DDepth_back.csv',
encoding='gbk')
#data1=pd.DataFrame({'frame':frames, 'angle_left':anglesl, 'angle_right':anglesr})
#data1.to_csv('testVedios'+'/test'+nb_vedio+'/'+number+'_LCR-NET_angles_3DKinect_back.csv',encoding='gbk')
else:
data2dd.to_csv('testVedios' + '/test' + nb_vedio + '/' + number +
'_LCR-NET_joints_2DDepth_front.csv',
encoding='gbk')
data2d.to_csv('testVedios' + '/test' + nb_vedio + '/' + number +
'_LCR-NET_joints_2DColor_front.csv',
encoding='gbk')
data.to_csv('testVedios' + '/test' + nb_vedio + '/' + number +
'_LCR-NET_joints_3DKinect_front.csv',
encoding='gbk')
def show_angle3d(nb_video, frames):
files=os.listdir('testVideos/test'+nb_video+'/')
for i in files:
if (len(re.findall('.*C\.json', i))!=0):
filejson=i
if (len(re.findall('.*D.mp4', i))!=0):
filedepth=i
number=filedepth[:-5]
if (len(re.findall('_PifPaf_joints_3DKinect_front.csv', i))!=0):
filedis=i
print(number)
print(filejson)
print(filedis)
print('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_3DKinect_back.csv')
disdata=pd.DataFrame(pd.read_csv('testVideos/test'+nb_video+'/'+filedis))
fig=plt.figure()
ax=fig.add_subplot(211)
ax2=fig.add_subplot(212)
ax.set_title('3d kinect PifPaf_Original')
ax.set_xlabel('Frame')
ax.set_ylabel('Angle_left')
ax2.set_xlabel('Frame')
ax2.set_ylabel('Angle_right')
with open('testVideos/test'+nb_video+'/'+filejson) as json_data:
d = json.load(json_data)
v=cv2.VideoCapture('testVideos/test'+nb_video+'/'+filedepth)
anglesl=[]
anglesr=[]
points3dx=[[] for p in range(17)]
points3dy=[[] for p in range(17)]
points3dz=[[] for p in range(17)]
points2dx=[[] for p in range(17)]
points2dy=[[] for p in range(17)]
points2ddx=[[] for p in range(17)]
points2ddy=[[] for p in range(17)]
akdis=[]
kndis=[]
step=[]
bad_frame=[0]*(frames[1]-frames[0])
l_kn_ak=[]
r_kn_ak=[]
for t in tqdm(range(frames[0],frames[1])):
#print(t)
v.set(cv2.CAP_PROP_POS_FRAMES, t)
ret, im=v.read()
img_m=cv2.medianBlur(im,5)
font=cv2.FONT_HERSHEY_SIMPLEX
'''
for i in range(800,980,2):
for j in range(300,860,5):
a=RGBto3D((i,j),im)
x.append(a[0])
y.append(a[1])
z.append(a[2])
print(i)
#ax.scatter(x,z,y)
'''
if len(d['frames'][t])!=0:
xjoints2d=d['frames'][t][0]['pose2d'][:17]
yjoints2d=d['frames'][t][0]['pose2d'][17:]
zjoints3d=[]
xjoints3d=[]
yjoints3d=[]
xjoints2dd=[]
yjoints2dd=[]
leg=['z_las','z_ras','z_lkn','z_rkn','z_lak','z_rak']
for i in range(17):
if i in [11,13,15,12,14,16]:
z3d=float(disdata[leg[i-11]][disdata['frames']==t])
print(z3d)
joints=tools.RGBto3D((xjoints2d[i], yjoints2d[i], z3d), im, t)
xjoints3d.append(joints[0])
yjoints3d.append(joints[1])
zjoints3d.append(joints[2])
else:
joints=tools.RGBto3D((xjoints2d[i], yjoints2d[i]), im, t, True, 7)
xjoints3d.append(joints[0])
yjoints3d.append(joints[1])
zjoints3d.append(joints[2])
joints2dd=tools.RGBtoD((xjoints2d[i], yjoints2d[i]), t)
xjoints2dd.append(joints2dd[0])
yjoints2dd.append(joints2dd[1])
anglesl.append(180-tools.angle((xjoints3d[11],yjoints3d[11],zjoints3d[11]),(xjoints3d[13],yjoints3d[13],zjoints3d[13]),(xjoints3d[15],yjoints3d[15],zjoints3d[15]),False))
anglesr.append(180-tools.angle((xjoints3d[12],yjoints3d[12],zjoints3d[12]),(xjoints3d[14],yjoints3d[14],zjoints3d[14]),(xjoints3d[16],yjoints3d[16],zjoints3d[16]),False))
# static analysis
# save the important information:dis between ankles, as height, leg length
l_kn_ak.append(tools.get_distance((xjoints3d[12],yjoints3d[12],zjoints3d[12]),(xjoints3d[13],yjoints3d[13],zjoints3d[13])))
r_kn_ak.append(tools.get_distance((xjoints3d[9],yjoints3d[9],zjoints3d[9]),(xjoints3d[10],yjoints3d[10],zjoints3d[10])))
dak=(zjoints3d[10]-zjoints3d[13])
dkn=(zjoints3d[9]-zjoints3d[12])
akdis.append(dak)
kndis.append(dkn)
for p in range(17):
points3dx[p].append(xjoints3d[p])
points3dy[p].append(yjoints3d[p])
points3dz[p].append(zjoints3d[p])
points2dx[p].append(xjoints2d[p])
points2dy[p].append(yjoints2d[p])
points2ddx[p].append(xjoints2dd[p])
points2ddy[p].append(yjoints2dd[p])
v.release()
#b, a = signal.butter(8, 0.3, 'lowpass')
#anglesl = signal.filtfilt(b, a, anglesl)
#anglesr = signal.filtfilt(b, a, anglesr)
frames=range(frames[0],frames[1])
ax.plot(frames, anglesl)
ax2.plot(frames, anglesr, color='r')
data2d={}
data={}
data2dd={}
data2dd['frames']=frames
data2d['frames']=frames
data['frames']=frames
data['kangle_l']=anglesl
data['kangle_r']=anglesr
data['akdis']=akdis
data['kndis']=kndis
data['l_kn_ak']=l_kn_ak
data['r_kn_ak']=r_kn_ak
data['bad_frame']=bad_frame
j=['head','leye','reye','lear','rear','lsh','rsh','lel','rel','lwr','rwr','las','ras','lkn','rkn','lak','rak']
for p in range(17):
data['x_'+j[p]]=points3dx[p]
data['y_'+j[p]]=points3dy[p]
data['z_'+j[p]]=points3dz[p]
data2d['x_'+j[p]]=points2dx[p]
data2d['y_'+j[p]]=points2dy[p]
data2dd['x_'+j[p]]=points2ddx[p]
data2dd['y_'+j[p]]=points2ddy[p]
data=pd.DataFrame(data)
data2d=pd.DataFrame(data2d)
data2dd=pd.DataFrame(data2dd)
if frames[0]>250:
data.to_csv('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_3DKinect_back.csv',encoding='gbk')
data2d.to_csv('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_2DColor_back.csv',encoding='gbk')
data2dd.to_csv('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_2DDepth_back.csv',encoding='gbk')
#data1=pd.DataFrame({'frame':frames, 'angle_left':anglesl, 'angle_right':anglesr})
#data1.to_csv('testVedios'+'/test'+nb_vedio+'/'+number+'_LCR-NET_angles_3DKinect_back.csv',encoding='gbk')
else:
data2dd.to_csv('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_2DDepth_front.csv',encoding='gbk')
data2d.to_csv('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_2DColor_front.csv',encoding='gbk')
data.to_csv('testVideos'+'/test'+nb_video+'/'+number+'_PifPaf_joints_3DKinect_front.csv',encoding='gbk')