json_dir = "../json/output"
model_dir = "models"
for f in os.listdir(json_dir):
os.remove(os.path.join(json_dir, f))
# 2. Run Openpose Webcam Mode
handler = subprocess.Popen([
openpose_demo_path, "--disable_blending=false", "--camera=" +
str(camera_offset), "--net_resolution=128x128", "--write_json=" + json_dir,
"--model_folder=" + model_dir, "--number_people_max=1"
],
shell=False)
print("Start 3 push-up")
tys = ["elbow", "arm", "shoulder"]
for ty in tys:
fds = FeedbackSystem()
fds.load("demo_front_" + ty + "_model", "front")
# 3. Give feedback
#try:
j = JsonParser()
video = j.parse(None, 60, json_dir, "front", None)
result = fds.feedback_kmeans(video, ty)
print(result)
handler.terminate()
#except:
# print("Exception Occured")
# handler.terminate()
"""
This code demonstrates simple learning and feedback process for wrong push-up posture.
For the intermediate presentations use only.
"""
from json_parser import JsonParser
from feedback import FeedbackSystem
from pathlib import Path
import os
# 1. learning FeedbackSystem with pre-labelled push-up data
fds = FeedbackSystem()
j = JsonParser()
#label format [partial range or not, elbow flare or not, wide or not]
videos_with_label = [("r0e0ns1", [0, 0, 0]), ("r0e0ns2", [0, 0, 0]),
("r0e0ns3", [0, 0, 0]), ("r0e0ws1", [0, 0, 1]),
("r0e0ws2", [0, 0, 1]), ("r0e0ws3", [0, 0, 1]),
("r0e1ns1", [0, 1, 0]), ("r0e1ns2", [0, 1, 0]),
("r0e1ws1", [0, 1, 1]), ("r0e1ws2", [0, 1, 1]),
("r0e1ws3", [0, 1, 1]), ("r1e0ns1", [1, 0, 0]),
("r1e0ns2", [1, 0, 0]), ("r1e0ns3", [1, 0, 0]),
("r1e0ns4", [1, 0, 0]), ("r1e0ws1", [1, 0, 1]),
("r1e0ws2", [1, 0, 1]), ("r1e0ws3", [1, 0, 1]),
("r1e1ns1", [1, 1, 0]), ("r1e1ns2", [1, 1, 0]),
("r1e1ns3", [1, 1, 0]), ("r1e1ws1", [1, 1, 1]),
("r1e1ws2", [1, 1, 1]), ("r1e1ws3", [1, 1, 1])]
tys = ["elbow", "shoulder", "arm"]
for ty in tys:
for video_with_label in videos_with_label:
path = Path("../json/" + video_with_label[0])
from json_parser import JsonParser
from video_processor import VideoProcessor
from feedback import FeedbackSystem
j = JsonParser()
video = j.parse("flare3", 200, "json/learn", "front", [0,0])
vp = VideoProcessor(video)
angles = vp.compute_left_elbow_angle(0.4)
fs = FeedbackSystem()
out = fs.min_max(angles)
print(out)
"""
This code demonstrates simple learning and feedback process for wrong push-up posture.
For the intermediate presentations use only.
"""
from json_parser import JsonParser
from feedback import FeedbackSystem
from pathlib import Path
# 1. learning FeedbackSystem with pre-labelled push-up data
fds = FeedbackSystem()
j = JsonParser()
#front_videos_with_label = [("correct1", 1), ("correct2", 1), ("correct3", 0), ("flare1", 1), ("flare2", 0), ("flare3", 0)]
videos_with_label = [("incorrect_squat", 1), ("correct_squat", 0)]
for video_with_label in videos_with_label:
path = Path("../json/" + video_with_label[0])
print(str(path))
video = j.parse(video_with_label[0], 200, path, "squat",
video_with_label[1])
fds.learn(video, threshold=0.5)
fds.save("demo_squat_model", "squat")
def start_feedback(self):
#time.sleep(5)
#collect data
print("feedback start")
print("GET READY")
time.sleep(3)
print("START")
#for i in reversed(range(self.sub2_layout.count())):
# self.sub2_layout.itemAt(i).widget().setParent(None)
#go_img = QLabel("GO")
#go_img.setPixmap(QPixmap("../pictures/go.JPG").scaledToWidth(320))
#go_img.setAlignment(Qt.AlignCenter)
#self.sub2_layout.addWidget(go_img)
start_point = len(os.listdir(json_dir))
j = JsonParser(start_point=start_point)
# incremental try
frame_no_list = [i*10 for i in range(4,10)]
err = 0
tys = ["elbow", "arm", "shoulder"]
result_dict = {}
for frame_no in frame_no_list:
print(str(frame_no) + " frame test")
video = j.parse(None, frame_no , json_dir, "front", None)
result_dict = {}
err = 0
for ty in tys:
print("doing " + ty)
fds = FeedbackSystem()
fds.load("demo_front_" + ty + "_model", "front")
result, div_zero = fds.feedback_kmeans(video, ty, threshold=0.3)
if div_zero:
err = 1
else:
result_dict[ty] = result
if err is 0:
break
if err is 1:
self.stop_op_screen("Posture is not detected. Please adjust webcam position")
return
fdm = FeedbackMsg(result_dict)
msg = fdm.get_feedback_msg()
#self.op_handler.terminate()
# now print out feedback msg
#self.stop_op_screen("Result")
need_cor = msg[0]
cor_msg = msg[1:]
#top_layout = QVBoxLayout()
#bottom_layout = QVBoxLayout()
"""
for m in cor_msg:
op_tmp = QLabel(m)
op_tmp.setAlignment(Qt.AlignCenter)
self.op_layout.addWidget(op_tmp)
"""
for i in reversed(range(self.sub2_layout.count())):
self.sub2_layout.itemAt(i).widget().setParent(None)
if need_cor:
bad_img = QLabel()
bad_img.setPixmap(QPixmap("../pictures/bad.JPG").scaledToWidth(260))
bad_img.setAlignment(Qt.AlignCenter)
self.sub2_layout.addWidget(bad_img)
else:
nice_img = QLabel()
nice_img.setPixmap(QPixmap("../pictures/nice.JPG").scaledToWidth(260))
nice_img.setAlignment(Qt.AlignCenter)
self.sub2_layout.addWidget(nice_img)
feedback_msg = ""
for m in cor_msg:
feedback_msg += m + "\n"
op_tmp = QLabel(feedback_msg)
op_tmp.setAlignment(Qt.AlignCenter)
op_tmp.setSizePolicy(QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed))
self.sub2_layout.addWidget(op_tmp)
"""
from feedback import FeedbackSystem
#label format [partial range, elbow flare, wide]
pr = 0
ef = 1
w = 0
min_dic = {}
max_dic = {}
min_dic["arm"]=70
max_dic["arm"]=76
min_dic["elbow"]=5
max_dic["elbow"]=180
min_dic["shoulder"]=94
max_dic["shoulder"]=120
tys = ["arm", "elbow", "shoulder"]
for ty in tys:
fds = FeedbackSystem()
fds.load("demo_front_" + ty + "_model", "front")
fds.manual_learn([min_dic[ty], max_dic[ty]], [pr,ef,w])
fds.save("demo_front_" + ty + "_model", "front")