def __init__(self, args):
self.args = args
# init openpose and 3d lifting model
self.opWrapper = op.WrapperPython()
params = dict(model_folder="/openpose/models/")
params['face'] = cfg.face
params['hand'] = False
params['model_pose'] = cfg.model_pose
params['num_gpu'] = 1
params['num_gpu_start'] = self.args.pid % cfg.ngpu
self.opWrapper.configure(params)
self.opWrapper.start()
self.poseLifting = Prob3dPose()
# cv bridge
self.bridge = CvBridge()
# subscriber and publisher
self.sub = rospy.Subscriber(
"/multi_proc_backend/info_{}".format(self.args.pid), ImageInfo,
self.callback)
self.pub = rospy.Publisher("/multi_proc_backend/result",
String,
tcp_nodelay=True,
queue_size=1)
def mesh(self, image):
image_h, image_w = image.shape[:2]
width = 640
height = 480
pose_2d_mpiis = []
visibilities = []
humans = self.e.inference(image, scales=[None])
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * width + 0.5), int(y * height + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
# pose_2d_mpiis = np.array(pose_2d_mpiis)
# visibilities = np.array(visibilities)
# transformed_pose2d, weights = self.poseLifting.transform_joints(
# pose_2d_mpiis, visibilities
# )
# pose_3d = self.poseLifting.compute_3d(transformed_pose2d, weights)
poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
print(pose_2d_mpiis)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
print(pose_3d)
keypoints = pose_3d[0].transpose()
return keypoints / 80
def Estimate_3Ddata(image, e, scales):
# t0 = time.time()
# # estimate human poses from a single image !
# t = time.time()
humans = e.inference(image, scales=scales)
#elapsed = time.time() - t
image = TfPoseEstimator.draw_humans(image, humans)
#logger.info('inference image:%.4f seconds.' % (elapsed))
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('lifting/models/prob_model_params.mat')
standard_w = 320
standard_h = 240
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
if (pose_2d_mpiis.ndim != 3):
return 0
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
#alltime= time.time() - t0
#logger.info('estimate all time:%.4f seconds.' % (alltime))
return pose_3d, image
def __init__(self):
"""
Initialize the graphics window and mesh surface
"""
# setup the view window
self.app = QtGui.QApplication(sys.argv)
self.window = gl.GLViewWidget()
self.window.setWindowTitle('Terrain')
self.window.setGeometry(0, 110, 1920, 1080)
self.window.setCameraPosition(distance=30, elevation=12)
self.window.show()
gx = gl.GLGridItem()
gy = gl.GLGridItem()
gz = gl.GLGridItem()
gx.rotate(90, 0, 1, 0)
gy.rotate(90, 1, 0, 0)
gx.translate(-10, 0, 0)
gy.translate(0, -10, 0)
gz.translate(0, 0, -10)
self.window.addItem(gx)
self.window.addItem(gy)
self.window.addItem(gz)
modeln = 'mobilenet_thin'
modelr = '432x368'
camera = 0
self.lines = {}
self.connection = [
[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6],
[0, 7], [7, 8], [8, 9], [9, 10], [8, 11], [11, 12],
[12, 13], [8, 14], [14, 15], [15, 16]
]
w, h = model_wh(modelr)
self.e = TfPoseEstimator(get_graph_path(modeln), target_size=(w, h))
self.cam = cv2.VideoCapture(camera)
ret_val, image = self.cam.read()
self.poseLifting = Prob3dPose('./skeleton_humanactivity/lifting/models/prob_model_params.mat')
keypoints = self.mesh(image)
self.points = gl.GLScatterPlotItem(
pos=keypoints,
color=pg.glColor((0, 255, 0)),
size=15
)
self.window.addItem(self.points)
for n, pts in enumerate(self.connection):
self.lines[n] = gl.GLLinePlotItem(
pos=np.array([keypoints[p] for p in pts]),
color=pg.glColor((0, 0, 255)),
width=3,
antialias=True
)
self.window.addItem(self.lines[n])
def process_multi(img_path, model):
image = common.read_imgfile(img_path, None, None)
filename = os.path.split(img_path)[1].split('.')[0]
scales = ast.literal_eval(node_or_string='[None]')
humans = model.inference(image, scales=scales)
image = model.draw_humans(image, humans, imgcopy=False)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
path_save_2d = path = './results/' + filename + '_2d.jpg'
cv2.imwrite(path_save_2d, image)
# cv2.imshow('result', image)
#cv2.waitKey()
#poseLifting = Prob3dPose('/data/ai/JF/pose_estimation/multi_pose_estimator/lifting/models/prob_model_params.mat')
poseLifting = Prob3dPose('./lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * image_w + 0.5), int(y * image_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
num = len(pose_3d)
if num % 2 == 0:
l = num // 2
else:
l = num // 2 + 1
IMAGES_PATH = './results/'
if not os.path.exists(IMAGES_PATH):
os.makedirs(IMAGES_PATH)
path_save_3d = './results/' + filename + '_3d.png'
fig = plt.figure()
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d, i, l, fig, num)
plt.savefig(path_save_3d)
# plt.show()
return path_save_2d, path_save_3d
def __init__(self):
"""
Initialize the graphics window and mesh surface
"""
# Initialize plot.
plt.ion()
f = plt.figure(figsize=(5, 5))
f2 = plt.figure(figsize=(6, 5))
self.window3DBody = f.gca(projection='3d')
self.window3DBody.set_title('3D_Body')
self.windowStable = f2.add_subplot(1, 1, 1)
self.windowStable.set_title('Stable')
self.windowStable.set_xlabel('Time')
self.windowStable.set_ylabel('Distant')
self.windowStable.set_ylim([0, 1500])
#plt.show()
self.times = [0]
self.stable = [0]
self.recordHead = []
self.fps_time = 0
model = 'mobilenet_thin_432x368'
w, h = model_wh(model)
#model = 'cmu'
#w,h = 656,368
camera = 1 #1 mean external camera , 0 mean internal camera
self.lines = {}
self.connection = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6],
[0, 7], [7, 8], [8, 9], [9, 10], [8, 11], [11, 12],
[12, 13], [8, 14], [14, 15], [15, 16]]
self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
self.cam = cv2.VideoCapture(camera)
ret_val, image = self.cam.read(cv2.IMREAD_COLOR)
self.poseLifting = Prob3dPose(
'./src/lifting/models/prob_model_params.mat')
self.statusBodyWindow = 0
try:
keypoints = self.mesh(image)
except:
pass
def __init__(self):
"""
Initialize the graphics window and mesh surface
"""
# setup the view window
self.app = QtGui.QApplication(sys.argv)
self.window = gl.GLViewWidget()
self.window.setWindowTitle('Terrain')
self.window.setGeometry(0, 110, 1920, 1080)
self.window.setCameraPosition(distance=30, elevation=12)
self.window.show()
gx = gl.GLGridItem()
gy = gl.GLGridItem()
gz = gl.GLGridItem()
gx.rotate(90, 0, 1, 0)
gy.rotate(90, 1, 0, 0)
gx.translate(-10, 0, 0)
gy.translate(0, -10, 0)
gz.translate(0, 0, -10)
self.window.addItem(gx)
self.window.addItem(gy)
self.window.addItem(gz)
model = 'mobilenet_thin'
camera = 0
w, h = model_wh("432x368")
self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
self.cam = cv2.VideoCapture(0)
ret_val, image = self.cam.read()
self.poseLifting = Prob3dPose('lifting/models/prob_model_params.mat')
keypoints = self.mesh(image)
self.points = gl.GLScatterPlotItem(
pos=keypoints,
color=pg.glColor((255, 255, 0)),
size=15
)
# self.lines = gl.GLLinePlotItem(
# pos=lines,
# color=pg.glColor((255, 255, 255)),
# width=2
# )
self.window.addItem(self.points)
def __init__(self):
"""
Initialize the graphics window and mesh surface
add some grids in the 3 point... that are just filling up the background
"""
# setup the view window
self.app = QtGui.QApplication(sys.argv)
self.window = gl.GLViewWidget()
self.window.setWindowTitle('Terrain')
self.window.setGeometry(0, 110, 1920, 1080)
self.window.setCameraPosition(distance=30, elevation=12)
self.window.show()
gx = gl.GLGridItem()
gy = gl.GLGridItem()
gz = gl.GLGridItem()
gx.rotate(90, 0, 1, 0)
gy.rotate(90, 1, 0, 0)
gx.translate(-10, 0, 0)
gy.translate(0, -10, 0) # we translate all of them
gz.translate(0, 0, -10) # so they're pushed out to the background
self.window.addItem(gx)
self.window.addItem(gy) # add them all to the window
self.window.addItem(gz)
model = "mobilenet_thin_432x368"
camera = 0
w, h = model_wh(model)
# we are gonna create e objects but instead we're gonna call it
self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
# we're basically just going the same thing(run.py line:37) instead of args.model
# we just created our own object for model
self.cam = cv2.VideoCapture(camera)
ret_val, image = self.cam.read()
self.poseLifting = Prob3dPose(
'./src/lifting/models/prob_model_params.mat')
# we'll have this object to do our 3d pose translater? yukardaki
keypoints = self.mesh(image)
self.points = gl.GLScatterPlotItem( # plot dots
pos=keypoints,
color=pg.glColor((0, 255, 0)),
size=15)
self.window.addItem(self.points)
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
plt.show()
import sys
sys.exit(0)
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5), int(y * standard_h + 0.5)) for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
def pose_ddd():
global pose_3d
u_r_s=20
u_l_s=180
left_elbow_dir=1
right_elbow_dir=1
while True:
try:
poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
#image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5), int(y * standard_h + 0.5)) for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
l_knee.update(pose_3d[0])
l_hip.update(pose_3d[0])
r_knee.update(pose_3d[0])
r_hip.update(pose_3d[0])
l_elbow.update(pose_3d[0])
r_elbow.update(pose_3d[0])
l_shoulder.update(pose_3d[0])
r_shoulder.update(pose_3d[0])
u_r_shoulder.update(pose_3d[0])
u_l_shoulder.update(pose_3d[0])
if u_r_shoulder.joint_angle < 30 :
u_r_s=90
elif u_r_shoulder.joint_angle > 30 :
u_r_s=20 # down
if u_l_shoulder.joint_angle < 30 :
u_l_s=90
elif u_l_shoulder.joint_angle > 30:
u_l_s=180 # down
#right 15:elbow , right 16: hand
if pose_3d[0][0,15] > pose_3d[0][0,16]:
right_elbow_dir=1
elif pose_3d[0][0,15] < pose_3d[0][0,16]:
right_elbow_dir=-1
if pose_3d[0][0,12] > pose_3d[0][0,13]:
left_elbow_dir=1
elif pose_3d[0][0,12] < pose_3d[0][0,13]:
left_elbow_dir=-1
message=str(r_hip.joint_angle)+','+str(r_knee.joint_angle-90)+','+str(l_hip.joint_angle)+','+str(l_knee.joint_angle-90)+','+str(right_elbow_dir*(r_elbow.joint_angle-140))+','+str(r_shoulder.joint_angle)+','+str(l_shoulder.joint_angle)+','+str(left_elbow_dir*(l_elbow.joint_angle-145))+','+str(u_r_s)+','+str(u_l_s)#","+str(l_knee.joint_angle-90)+','+str(r_hip.joint_angle)+","+str(r_knee.joint_angle-90)
clientsocket.send(message.encode('utf-8'))
if BREAK == True:
clientsocket.send("off".encode('utf-8'))
print("off sent")
break
except :
pass
def adHocData(self):
directory_in_str = sys.path[0] + "\\..\\images\\OurTest\\"
# Delete old csv files
try:
os.remove(outputfile)
os.remove(cleanedOutputfile)
except OSError:
pass
# Run through every image in the folder
for file in os.listdir(directory_in_str):
filename = os.fsdecode(file)
if filename.endswith(".jpg") or filename.endswith(".png"):
fullpath = directory_in_str + filename
# Estimate human poses from a single image !
image = common.read_imgfile(fullpath, None, None)
# image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
t = time.time()
humans = self.e.inference(image, scales=self.scales)
elapsed = time.time() - t
logger.info('inference image: %s in %.4f seconds.' %
(fullpath, elapsed))
image = TfPoseEstimator.draw_humans(image,
humans,
imgcopy=False)
# cv2.imshow('tf-pose-estimation result', image)
# cv2.waitKey()
myFile = open(outputfile, 'a')
# myFile.write(str(filename) + ',')
# print(filename)
myFile.write('\n')
myFile.close()
# Attempt to plot our skeletons onto the image
try:
fig = plt.figure()
a = fig.add_subplot(2, 2, 1)
a.set_title('Result')
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
bgimg = cv2.cvtColor(image.astype(np.uint8),
cv2.COLOR_BGR2RGB)
bgimg = cv2.resize(
bgimg,
(self.e.heatMat.shape[1], self.e.heatMat.shape[0]),
interpolation=cv2.INTER_AREA)
# show network output
a = fig.add_subplot(2, 2, 2)
plt.imshow(bgimg, alpha=0.5)
tmp = np.amax(self.e.heatMat[:, :, :-1], axis=2)
plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
tmp2 = self.e.pafMat.transpose((2, 0, 1))
tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0)
tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0)
a = fig.add_subplot(2, 2, 3)
a.set_title('Vectormap-x')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
plt.show()
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose(
sys.path[0] +
'\\lifting\\models\\prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(
human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d,
weights)
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d)
plt.show()
except:
print("Error when plotting image ")
dataScriptGenerator.dataCleanup(self)
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
plt.show()
import sys
sys.exit(0)
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose(
'./pose_estimation/lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
def __init__(self):
"""
Initialize the graphics window and mesh surface
add some grids in the 3 point... that are just filling up the background
"""
# setup the view window
self.app = QtGui.QApplication(sys.argv)
self.window = gl.GLViewWidget()
self.window.setFixedSize(1920, 1080)
self.window.showMaximized()
# self.window.setMaximumSize(1920, 1080)
# self.win = pg.GraphicsWindow()
# self.vb = self.win.addViewBox(col=0, row=0)
# self.t = pg.TextItem("zeynep", (255, 255, 255), anchor=(0,0))
# self.vb.addItem(self.t)
self.window.setWindowTitle('Terrain')
self.window.setGeometry(0, 0, 1920, 1080)
self.window.setCameraPosition(distance=30, elevation=12)
self.window.show()
# self.root = Tk()
self.angle = ""
# self.root = Tk()
# self.root.geometry("100x50+%d+%d" % (0, 0)) # top left
# self.root.overrideredirect(True) # frameless tkinter window
# self.root.resizable(False, False)
# self.root.columnconfigure(0, weight=1)
# self.root.rowconfigure(0, weight=1)
# TEXT = ""
# self.lbl = tk.Label(root, text=TEXT, bg="#e61c1c", font=("bold", 15), border=0, width=35)
# self.lbl.grid(column=0, row=0, sticky="nsew")
gx = gl.GLGridItem()
gy = gl.GLGridItem()
gz = gl.GLGridItem()
gx.rotate(90, 0, 1, 0)
gy.rotate(90, 1, 0, 0)
gx.translate(-10, 0, 0)
gy.translate(0, -10, 0) # we translate all of them
gz.translate(0, 0, -10) # so they're pushed out to the background
self.window.addItem(gx)
self.window.addItem(gy) # add them all to the window
self.window.addItem(gz)
model = "mobilenet_thin_432x368"
camera = 0
self.lines = {}
self.connections = [ # lines that we want to connect all those key points
[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6], [0, 7], [7, 8],
[8, 9], [9, 10], [8, 11], [11, 12], [12, 13], [8, 14], [14, 15],
[15, 16]
] # 5.35
w, h = model_wh(model)
# we are gonna create e objects but instead we're gonna call it
self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
# we're basically just going the same thing(run.py line:37) instead of args.model
# we just created our own object for model
self.cam = cv2.VideoCapture(camera)
ret_val, image = self.cam.read()
self.poseLifting = Prob3dPose(
'./src/lifting/models/prob_model_params.mat')
# we'll have this object to do our 3d pose translater? yukardaki
keypoints = self.mesh(image)
# rightPointList = keypoints[2:5]
self.rightPointList = keypoints[11:14]
self.leftPointList = keypoints[14:]
self.a = (
gl.GLScatterPlotItem( # plot dot
pos=keypoints[:11],
color=pg.glColor((0, 255, 0)),
size=15))
self.window.addItem(self.a)
self.right = (
gl.GLScatterPlotItem( # plot dot
pos=self.rightPointList,
color=pg.glColor((255, 0, 0)),
size=15))
self.window.addItem(self.right)
self.left = (
gl.GLScatterPlotItem( # plot dot
pos=self.leftPointList,
color=pg.glColor((0, 0, 255)),
size=15))
self.window.addItem(self.left)
for n, pts in enumerate(self.connections):
self.lines[n] = gl.GLLinePlotItem( # lines dict with all of them
pos=np.array([keypoints[p] for p in pts]),
color=pg.glColor((0, 0, 255)),
width=3,
antialias=True)
# add them to our window
self.window.addItem(self.lines[n])
def __init__(self):
# setup the view window
self.app = QtGui.QApplication(sys.argv)
self.window = gl.GLViewWidget()
self.window.setFixedSize(1920, 1080)
# self.window.showMaximized()
self.window.setWindowTitle('Terrain')
self.window.setGeometry(0, 0, 1920, 1080)
self.window.setCameraPosition(distance=30, elevation=12)
self.window.show()
self.angle = ""
gx = gl.GLGridItem()
gy = gl.GLGridItem()
gz = gl.GLGridItem()
gx.rotate(90, 0, 1, 0)
gy.rotate(90, 1, 0, 0)
gx.translate(-10, 0, 0)
gy.translate(0, -10, 0) # we translate all of them
gz.translate(0, 0, -10) # so they're pushed out to the background
self.window.addItem(gx)
self.window.addItem(gy) # add them all to the window
self.window.addItem(gz)
model = "mobilenet_thin_432x368"
camera = 0
self.lines = {}
self.connections = [ # lines that we want to connect all those key points
[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6], [0, 7], [7, 8],
[8, 9], [9, 10], [8, 11], [11, 12], [12, 13], [8, 14], [14, 15],
[15, 16]
] # 5.35
w, h = model_wh(model)
# we are gonna create e objects but instead we're gonna call it
print("modellllll : ", get_graph_path(model))
self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
print("self.e : ", self.e)
# we're basically just going the same thing(run.py line:37) instead of args.model
# we just created our own object for model
self.cam = cv2.VideoCapture(camera)
ret_val, image = self.cam.read()
self.poseLifting = Prob3dPose(
'./src/lifting/models/prob_model_params.mat')
# we'll have this object to do our 3d pose translater? yukardaki
keypoints = self.mesh(image)
self.rightPointList = keypoints[11:14]
self.leftPointList = keypoints[14:]
self.a = (
gl.GLScatterPlotItem( # plot dot
pos=keypoints[:11],
color=pg.glColor((0, 255, 0)),
size=15))
self.window.addItem(self.a)
self.right = (
gl.GLScatterPlotItem( # plot dot
pos=self.rightPointList,
color=pg.glColor((255, 0, 0)),
size=15))
self.window.addItem(self.right)
self.left = (
gl.GLScatterPlotItem( # plot dot
pos=self.leftPointList,
color=pg.glColor((0, 0, 255)),
size=15))
self.window.addItem(self.left)
for n, pts in enumerate(self.connections):
self.lines[n] = gl.GLLinePlotItem( # lines dict with all of them
pos=np.array([keypoints[p] for p in pts]),
color=pg.glColor((0, 0, 255)),
width=3,
antialias=True)
# add them to our window
self.window.addItem(self.lines[n])
def __init__(self):
"""
Initialize the graphics window and mesh surface
"""
# setup the view window
self.app = QtGui.QApplication(sys.argv)
self.window = gl.GLViewWidget()
self.window.setWindowTitle('Terrain')
self.window.setGeometry(0, 110, 1920, 1080)
self.window.setCameraPosition(distance=30, elevation=12)
self.window.show()
gx = gl.GLGridItem()
gy = gl.GLGridItem()
gz = gl.GLGridItem()
gx.rotate(90, 0, 1, 0)
gy.rotate(90, 1, 0, 0)
gx.translate(-10, 0, 0)
gy.translate(0, -10, 0)
gz.translate(0, 0, -10)
self.window.addItem(gx)
self.window.addItem(gy)
self.window.addItem(gz)
model = 'mobilenet_thin'
camera = 0
self.lines = {}
self.connection = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6],
[0, 7], [7, 8], [8, 9], [9, 10], [8, 11], [11, 12],
[12, 13], [8, 14], [14, 15], [15, 16]]
parser = argparse.ArgumentParser(
description='tf-pose-estimation webcam3D')
parser.add_argument(
'--resize',
type=str,
default='0x0',
help='if provided, resize images before they are processed. '
'default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ')
parser.add_argument('--model',
type=str,
default='mobilenet_thin',
help='cmu / mobilenet_thin')
parser.add_argument('--resolution',
type=str,
default='432x368',
help='network input resolution. default=432x368')
args = parser.parse_args()
w, h = model_wh('432x368')
#w, h = model_wh(model)
self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
self.cam = cv2.VideoCapture(camera)
ret_val, image = self.cam.read()
self.poseLifting = Prob3dPose(
'./src/lifting/models/prob_model_params.mat')
keypoints = self.mesh(image)
self.points = gl.GLScatterPlotItem(pos=keypoints,
color=pg.glColor((0, 255, 0)),
size=15)
self.window.addItem(self.points)
for n, pts in enumerate(self.connection):
self.lines[n] = gl.GLLinePlotItem(pos=np.array(
[keypoints[p] for p in pts]),
color=pg.glColor((0, 0, 255)),
width=3,
antialias=True)
self.window.addItem(self.lines[n])
def main():
parser = argparse.ArgumentParser(description='tf-pose-estimation run')
parser.add_argument('--image', type=str, default='./images/p3.jpg')
parser.add_argument(
'--resolution',
type=str,
default='432x368',
help='network input resolution. default=432x368')
parser.add_argument(
'--model',
type=str,
default='mobilenet_thin',
help='cmu / mobilenet_thin')
parser.add_argument(
'--scales',
type=str,
default='[None]',
help='for multiple scales, eg. [1.0, (1.1, 0.05)]')
parser.add_argument(
'--stick_only',
action='store_true',
help='save output without other analysis')
parser.add_argument('--plot_3d', action='store_true', help='save 3d poses')
args = parser.parse_args()
scales = ast.literal_eval(args.scales)
w, h = model_wh(args.resolution)
e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
# estimate human poses from a single image !
image = common.read_imgfile(args.image, None, None)
# image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
t = time.time()
humans = e.inference(image, scales=scales)
elapsed = time.time() - t
logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed))
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
# cv2.imshow('tf-pose-estimation result', image)
# cv2.waitKey()
import matplotlib.pyplot as plt
fig = plt.figure()
a = fig.add_subplot(2, 2, 1)
a.set_title('Result')
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.stick_only:
cv2.imwrite('output/test.png', image)
logger.info('image saved: {}'.format('output/test.png'))
import sys
sys.exit(0)
plt.imshow(rgb_image)
bgimg = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2RGB)
bgimg = cv2.resize(
bgimg, (e.heatMat.shape[1], e.heatMat.shape[0]),
interpolation=cv2.INTER_AREA)
# show network output
a = fig.add_subplot(2, 2, 2)
plt.imshow(bgimg, alpha=0.5)
tmp = np.amax(e.heatMat[:, :, :-1], axis=2)
plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
tmp2 = e.pafMat.transpose((2, 0, 1))
tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0)
tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0)
a = fig.add_subplot(2, 2, 3)
a.set_title('Vectormap-x')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
if not os.path.exists('output'):
os.mkdir('output')
plt.savefig('output/test.png')
logger.info('image saved: {}'.format('output/test.png'))
if not args.plot_3d:
import sys
sys.exit(0)
#For plotting in 3d
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d)
plt.draw()
plt.savefig('output/pose_3d_test.png')
logger.info('3d plot saved: {}'.format('output/pose_3d_test.png'))
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
plt.show()
# import sys
# sys.exit(0)
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
print('humans count' + str(len(humans)))
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
