def main(VideoName):
cap, cap_length = videoInfo(VideoName)
kpt2Ds = []
for i in tqdm(range(cap_length)):
_, frame = cap.read()
frame, W, H = resize_img(frame)
try:
joint2D = interface2D(frame, model2D)
except Exception as e:
print(e)
continue
if i == 0:
for _ in range(30):
kpt2Ds.append(joint2D)
elif i < 30:
kpt2Ds.append(joint2D)
kpt2Ds.pop(0)
else:
kpt2Ds.append(joint2D)
joint3D = interface3D(model3D, np.array(kpt2Ds), W, H)
joint3D_item = joint3D[-1] #(17, 3)
draw_3Dimg(joint3D_item, frame, display=1, kpt2D=joint2D)
def main(VideoName):
cap, cap_length, cap_fps = videoInfo(VideoName)
kpt2Ds = []
for i in tqdm(range(cap_length)):
_, frame = cap.read()
frame, W, H = resize_img(frame)
try:
t0 = time.time()
joint2D = interface2D(bboxModel, poseModel, frame)
print('HrNet comsume {:0.3f} s'.format(time.time() - t0))
except Exception as e:
print(e)
continue
if i == 0:
for _ in range(30):
kpt2Ds.append(joint2D)
elif i < 30:
kpt2Ds.append(joint2D)
kpt2Ds.pop(0)
else:
kpt2Ds.append(joint2D)
joint3D = interface3D(model3D, np.array(kpt2Ds), W, H)
joint3D_item = joint3D[-1] #(17, 3)
draw_3Dimg(joint3D_item, frame, display=1, kpt2D=joint2D)
def Display(self):
self.ui.Open.setEnabled(False)
self.ui.Close.setEnabled(True)
while self.cap.isOpened():
success, frame = self.cap.read()
frame, W, H = resize_img(frame)
try:
# get 2d pose keypoints from image using pose estimator (hrnet)
joint2D = interface2D(bboxModel, poseModel, frame)
except Exception as e:
print(e)
continue
# draw pose keypoints into source image
img = draw_2Dimg(frame, joint2D, None)
# RGB to BGR
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img_out = QImage(img_bgr.data, W, H, QImage.Format_RGB888)
self.ui.DisplayLabel.setPixmap(QPixmap.fromImage(img_out))
if self.isCamera:
cv2.waitKey(1)
else:
cv2.waitKey(int(1000 / self.frameRate))
if self.stopEvent.is_set() == True:
self.stopEvent.clear()
self.ui.DisplayLabel.clear()
self.ui.Close.setEnabled(False)
self.ui.Open.setEnabled(True)
self.cap.release()
break
def main(VideoName, model_layes):
model = posenet.load_model(args.model)
model = model.cuda()
output_stride = model.output_stride
cap, cap_length = videoInfo(VideoName)
kpt2Ds = []
pose_3d = []
#annotator = AnnotatorInterface.build(max_persons=1)
for i in range(cap_length): #tqdm(range(cap_length)):
#if i < 90: continue
if i > 300: break
_, frame = cap.read()
input_image, display_image, output_scale = posenet.process_input(
frame, 1 / 3.0, output_stride)
frame, W, H = resize_img(frame)
time0 = time.time()
joint2D = get_2d_pose_torch(input_image, output_stride,
model) #get_2d_pose_1(frame)
time1 = time.time()
#print(output_scale)
#joint2 = 0#get_2d_pose_2(sess, input_image, output_stride, model_outputs)
#persons = annotator.update(frame)
#poses_2d = [p['pose_2d'].get_joints() for p in persons]
#joint2D2 = poses_2d[0]
#print(joint2D)
#joint2D = np.vstack((joint2D[0:1, :], joint2D[5:17, :]))
#print(joint2D3.shape)
time2 = time.time()
#raise KeyboardInterrupt
if i == 0:
for _ in range(30):
kpt2Ds.append(joint2D)
else:
kpt2Ds.append(joint2D)
kpt2Ds.pop(0)
#if i < 15:
# kpt2Ds.append(joint2D)
# kpt2Ds.pop(0)
#else:
# kpt2Ds.append(joint2D)
#print(len(kpt2Ds))
joint3D = interface3D(model3D, np.array(kpt2Ds), W, H)
joint3D_item = joint3D[-1] #(17, 3)
time3 = time.time()
pose_3d.append((joint3D_item, joint2D))
print(time1 - time0, time2 - time1, time3 - time2, time3 - time1)
#draw_3Dimg(joint3D_item, frame, display=1, kpt2D=joint2D)
save_pose(pose_3d)
def VideoPoseJoints(VideoName):
cap, cap_length = videoInfo(VideoName)
kpt2Ds = []
for i in tqdm(range(cap_length)):
_, frame = cap.read()
frame, W, H = resize_img(frame)
try:
joint2D = interface2D(frame, model2D)
except Exception as e:
print(e)
continue
draw_2Dimg(frame, joint2D, 1)
kpt2Ds.append(joint2D)
joint3D = interface3D(model3D, np.array(kpt2Ds), W, H)
return joint3D
def main(VideoName):
# frame = cv2.imread(VideoName, 1)
# joint2D = interface2D(bboxModel, poseModel, frame)
# img = draw_2Dimg(frame, joint2D, None)
# cv2.imwrite('out_test_9.jpg', img)
# cap, cap_length = videoInfo(VideoName)
cap = cv2.VideoCapture(0)
kpt2Ds = []
queueSize = 30
# for i in tqdm(range(cap_length)):
i = 0
while (True):
_, frame = cap.read()
frame, W, H = resize_img(frame)
try:
t0 = time.time()
joint2D = interface2D(bboxModel, poseModel, frame)
except Exception as e:
print(e)
continue
if i == 0:
for _ in range(queueSize):
kpt2Ds.append(joint2D)
elif i < queueSize:
kpt2Ds.append(joint2D)
kpt2Ds.pop(0)
else:
kpt2Ds.append(joint2D)
# joint3D = interface3D(model3D, np.array(kpt2Ds), W, H)
# joint3D_item = joint3D[-1] #(17, 3)
# draw_3Dimg(joint3D_item, frame, display=1, kpt2D=joint2D)
img = draw_2Dimg(frame, joint2D, None)
cv2.imshow('im', img)
# i = i+1
if cv2.waitKey(1) & 0xff == ord('q'):
cap.release()
break
print('total comsume {:0.3f} s'.format(time.time() - t0))
def main():
# use camera
cap = cv2.VideoCapture(0)
while True:
# read every frame from camera
_, frame = cap.read()
frame, W, H = resize_img(frame)
try:
# get 2d pose keypoints from image using pose estimator (hrnet)
joint2D = interface2D(bboxModel, poseModel, frame)
except Exception as e:
print(e)
continue
# draw pose keypoints into source image
img = draw_2Dimg(frame, joint2D, None)
cv2.imshow('result_view', img)
# exit control
if cv2.waitKey(1) & 0xff == ord('q'):
cap.release()
break
def update(self):
global item
global item_num
num = item / 2
azimuth_value = abs(num % 120 + math.pow(-1, int((num / 120))) * 120) % 120
self.w.opts['azimuth'] = azimuth_value
print(item, ' ')
_, frame = self.cap.read()
if item % 2 != 1:
frame, W, H = resize_img(frame)
joint2D = interface2D(frame, model2D)
img2D = draw_2Dimg(frame, joint2D, 1)
if item == 0:
for _ in range(30):
self.kpt2Ds.append(joint2D)
elif item < 30:
self.kpt2Ds.append(joint2D)
self.kpt2Ds.pop(0)
else:
self.kpt2Ds.append(joint2D)
self.kpt2Ds.pop(0)
item += 1
joint3D = interface3D(model3D, np.array(self.kpt2Ds), W, H)
pos = joint3D[-1] # (17, 3)
for j, j_parent in enumerate(common.skeleton_parents):
if j_parent == -1:
continue
x = np.array([pos[j, 0], pos[j_parent, 0]]) * 10
y = np.array([pos[j, 1], pos[j_parent, 1]]) * 10
z = np.array([pos[j, 2], pos[j_parent, 2]]) * 10 - 10
pos_total = np.vstack([x, y, z]).transpose()
self.set_plotdata(
name=j, points=pos_total,
color=pg.glColor((j, 10)),
width=6)
# save
if item_num < 10:
name = '000' + str(item_num)
elif item_num < 100:
name = '00' + str(item_num)
elif item_num < 1000:
name = '0' + str(item_num)
else:
name = str(item_num)
im3Dname = 'VideoSave/' + '3D_' + name + '.png'
d = self.w.renderToArray((img2D.shape[1], img2D.shape[0])) # (W, H)
print('Save 3D image: ', im3Dname)
pg.makeQImage(d).save(im3Dname)
im2Dname = 'VideoSave/' + '2D_' + name + '.png'
print('Save 2D image: ', im2Dname)
cv2.imwrite(im2Dname, img2D)
item_num += 1
else:
item += 1
def main(_):
global framenum
#clear out all old frames
os.system("rm png/*")
#set done to empty array, it will hold the json files from openpose that we've already processed
done = []
#initialize input tensor to 1x64 array of zeroes [[0. 0. 0. ...]]
#this is list of numpy vectors to feed as encoder inputs (32 2d coordinates)
enc_in = np.zeros((1, 64))
enc_in[0] = [0 for i in range(64)]
#actions to run on, default is all
actions = data_utils.define_actions(FLAGS.action)
#the list of Human3.6m subjects to look at
SUBJECT_IDS = [1, 5, 6, 7, 8, 9, 11]
#load camera parameters from the h36m dataset
rcams = cameras2.load_cameras(FLAGS.cameras_path, SUBJECT_IDS)
#loads 2d data from precomputed Stacked Hourglass detections
train_set_2d, test_set_2d, data_mean_2d, data_std_2d, dim_to_ignore_2d, dim_to_use_2d = data_utils.read_2d_predictions(
actions, FLAGS.data_dir)
#loads 3d poses, zero-centres and normalizes them
train_set_3d, test_set_3d, data_mean_3d, data_std_3d, dim_to_ignore_3d, dim_to_use_3d, train_root_positions, test_root_positions = data_utils.read_3d_data(
actions, FLAGS.data_dir, FLAGS.camera_frame, rcams, FLAGS.predict_14)
device_count = {"GPU": 0}
png_lib = []
#run a tensorflow inference session
with tf.Session(config=tf.ConfigProto(device_count=device_count,
allow_soft_placement=True)) as sess:
#plt.figure(3)
#load pre-trained model
batch_size = 128
model = create_model(sess, actions, batch_size)
#infinitely show 3d pose visualization
while True:
#wait for key to be pressed
key = cv2.waitKey(1) & 0xFF
_, frame = cv2.VideoCapture(
0).read() #ignore the other returned value
#resize and rotate the incoming image frame
frame, W, H = resize_img(frame)
frame = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE)
start = time.time()
#run posenet inference on the frame
joints_2d = estimate_pose(frame)
#throw out confidence score and flatten
_data = joints_2d[..., :2].flatten()
#open pop-up and draw the keypoints found
img2D = draw_2Dimg(frame, joints_2d, 1)
#fake the thorax point by finding midpt between left and right shoulder
lt_should_x = _data[10]
lt_should_y = _data[11]
rt_should_x = _data[12]
rt_should_y = _data[13]
thorax = midpoint(lt_should_x, lt_should_y, rt_should_x,
rt_should_y)
#print("testing thorax pt at ", thorax)
#insert thorax into data where it should be, at index 1
_data = np.insert(_data, 2, thorax[0])
_data = np.insert(_data, 3, thorax[1])
#print("new _data is ", _data)
_data = np.around(_data)
#set xy to the array of 2d joint data
xy = _data
#create new 1x36 array of zeroes, which will store the 18 2d keypoints
joints_array = np.zeros((1, 36))
joints_array[0] = [0 for i in range(36)]
#index into our data array
index = 0
#iterates 18 times
for o in range(int(len(joints_array[0]) / 2)):
#feed array with xy array (the 18 keypoints), but switch ordering: posenet to openpose
for j in range(2):
#print("o is", o, "j is", j, "index is ", index)
index_into_posenet_data = order_pnet_to_openpose[o] * 2 + j
#print("putting posenet[", index_into_posenet_data, "], value ", xy[index_into_posenet_data], " , into joints_array[0][", index, "]")
joints_array[0][index] = xy[index_into_posenet_data]
index += 1
#set _data to the array containing the 36 coordinates of the 2d keypts
_data = joints_array[0]
#print("_data is ", _data)
#mapping all body parts for 3d-pose-baseline format (32 2d coordinates)
for i in range(len(order)): #iterates 14 times
#select which coordinateof this point: x or y
for j in range(2):
#create encoder input, switching around the order of the joint points
enc_in[0][order[i] * 2 + j] = _data[i * 2 + j]
#now enc_in contains 14 points (28 total coordinates)
#at this pt enc_in should be array of 64 vals
for j in range(2):
#place hip at index 0
enc_in[0][0 * 2 + j] = (enc_in[0][1 * 2 + j] +
enc_in[0][6 * 2 + j]) / 2
#place neck/nose at index 14
enc_in[0][14 * 2 + j] = (enc_in[0][15 * 2 + j] +
enc_in[0][12 * 2 + j]) / 2
#place thorax at index 13
enc_in[0][13 * 2 +
j] = 2 * enc_in[0][12 * 2 + j] - enc_in[0][14 * 2 +
j]
#set spine found by openpose
spine_x = enc_in[0][24]
spine_y = enc_in[0][25]
#dim_to_use_2d is always [0 1 2 3 4 5 6 7 12 13 14 15 16 17 24 25 26 27 30 31 34 35 36 37 38 39 50 51 52 53 54 55]
#take 32 entries of enc_in
enc_in = enc_in[:, dim_to_use_2d]
#find mean of 2d data
mu = data_mean_2d[dim_to_use_2d]
#find stdev of 2d data
stddev = data_std_2d[dim_to_use_2d]
#subtract mean and divide std for all
enc_in = np.divide((enc_in - mu), stddev)
#dropout keep probability
dp = 1.0
#output tensor, initialize it to zeroes. We'll get 16 joints with 3d coordinates
#this is list of numpy vectors that are the expected decoder outputs
dec_out = np.zeros((1, 48))
dec_out[0] = [0 for i in range(48)]
#get the 3d poses by running the 3d-pose-baseline inference. Model operates on 32 points
_, _, poses3d = model.step(sess,
enc_in,
dec_out,
dp,
isTraining=False)
#poses3d comes back as a 1x96 array (I guess its 32 points)
end = time.time()
#print("ELAPSED: ", end-start)
#hold our 3d poses while we're doing some post-processing
all_poses_3d = []
#un-normalize the input and output data using the means and stdevs
enc_in = data_utils.unNormalizeData(enc_in, data_mean_2d,
data_std_2d, dim_to_ignore_2d)
poses3d = data_utils.unNormalizeData(poses3d, data_mean_3d,
data_std_3d, dim_to_ignore_3d)
#create a grid for drawing
gs1 = gridspec.GridSpec(1, 1)
#set spacing between axes
gs1.update(wspace=-0.00, hspace=0.05)
plt.axis('off')
#fill all_poses_3d with the 3d poses predicted by the model step fxn
all_poses_3d.append(poses3d)
#vstack stacks arrays in sequence vertically (row wise)
#this doesn't do anything in this case, as far as I can tell
enc_in, poses3d = map(np.vstack, [enc_in, all_poses_3d])
subplot_idx, exidx = 1, 1
_max = 0
_min = 10000
#iterates once
for i in range(poses3d.shape[0]):
#iterate over all 32 points in poses3d
for j in range(32):
#save the last coordinate of this point into tmp
tmp = poses3d[i][j * 3 + 2]
#swap the second and third coordinates of this pt
poses3d[i][j * 3 + 2] = poses3d[i][j * 3 + 1]
poses3d[i][j * 3 + 1] = tmp
#keep track of max of last coordinate
if poses3d[i][j * 3 + 2] > _max:
_max = poses3d[i][j * 3 + 2]
if poses3d[i][j * 3 + 2] < _min:
_min = poses3d[i][j * 3 + 2]
#iterates once
for i in range(poses3d.shape[0]):
#iterate over all 32 points in poses3d (2nd and 3rd coords have all been swapped at this pt)
for j in range(32):
#change the third coord of this pt, subtracting it from sum of max and min third coord to get new value
poses3d[i][j * 3 + 2] = _max - poses3d[i][j * 3 + 2] + _min
#modify first coord of this pt by adding the x coord of the spine found by 2d model
poses3d[i][j * 3] += (spine_x - 630)
#modify third coord of this pt by adding 500 minus y coord of spine found by 2d model
poses3d[i][j * 3 + 2] += (500 - spine_y)
#Plot 3d predictions
ax = plt.subplot(gs1[subplot_idx - 1], projection='3d')
ax.view_init(18, -70)
logger.debug(np.min(poses3d))
#TODO: if something happened with the data, reuse data from last frame (before_pose)
p3d = poses3d
#plot the 3d skeleton
viz.show3Dpose(p3d, ax, lcolor="#9b59b6", rcolor="#2ecc71")
#keep track of this poses3d in case we need to reuse it for next frame
before_pose = poses3d
#save this frame as a png in the ./png/ folder
pngName = 'png/test_{0}.png'.format(str(framenum))
#print("pngName is ", pngName)
plt.savefig(pngName)
#plt.show()
#read this frame which was just saved as png
img = cv2.imread(pngName, 0)
rect_cpy = img.copy()
#show this frame
cv2.imshow('3d-pose-baseline', rect_cpy)
framenum += 1
#quit if q is pressed
if key == ord('q'):
break
sess.close()
def update(self):
#these globals get updated on every callback
global item
global item_num
#set num to half of item
num = item/2
#calculate camera's current azimuthal angle in degrees and store it in the Visualizer item
#azimuth_value = abs(num%120 + math.pow(-1, int((num/120))) * 120) % 120
#self.w.opts['azimuth'] = azimuth_value
#Log which frame this is
#print("Frame #", item)
#read in a frame from the VideoCapture (webcam)
_, frame = self.cap.read() #ignore the other returned value
frame = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE)
#update every other frame
if item % 2 != 1:
#resize the incoming image frame
frame, W, H = resize_img(frame)
start = time.time()
#run Posenet inference to find the 2D joint keypoints
joints_2D = estimate_pose(frame)
#open pop-up and draw the keypoints found
img2D = draw_2Dimg(frame, joints_2D, 1)
#if this is the first frame
if item == 0:
for _ in range(30):
self.kpt2Ds.append(joints_2D)
else:
self.kpt2Ds.append(joints_2D)
self.kpt2Ds.pop(0)
#increment the frame counter
item += 1
#run 2D-3D inference using VideoPose3D model
joint3D = interface3d(model3D, np.array(self.kpt2Ds), W, H)
end = time.time()
#print("ELAPSED:", end-start)
#get the 3d coordinates
pos = joint3D[-1] #(17, 3)
for j, j_parent in enumerate(common.skeleton_parents):
if j_parent == -1:
continue
x = np.array([pos[j, 0], pos[j_parent, 0]]) * 10
y = np.array([pos[j, 1], pos[j_parent, 1]]) * 10
z = np.array([pos[j, 2], pos[j_parent, 2]]) * 10 - 10
pos_total = np.vstack([x,y,z]).transpose()
self.set_plotdata(name=j, points=pos_total, color=pg.glColor((j, 10)), width=6)
d = self.w.renderToArray((img2D.shape[1], img2D.shape[0])) #(W, H)
item_num += 1
else:
item += 1
