def upload_file():
if request.method == 'POST':
# if 'file' not in request.files:
# flash('No file part')
# return redirect(request.url)
#
print(request.files.keys())
print(request.files['data'])
file = request.files['data']
print(dir(file))
print(file.filename)
file.save(os.path.join(os.getcwd(), 'uploads.mp4'))
try:
try:
import pyopenpose as op
except ImportError as e:
raise e
try:
import cv2
except ImportError as e:
raise e
try:
from test_video import SampleVideo
except ImportError as e:
raise e
params = dict()
params["model_folder"] = "C:\\openpose\\models\\"
params["number_people_max"] = 1
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
datum = op.Datum()
video_path = os.path.join(os.getcwd(), 'uploads.mp4')
cap = cv2.VideoCapture(video_path)
print(cap.get(cv2.CAP_PROP_FRAME_COUNT))
out_path = os.path.join(os.getcwd(), 'static/output.mp4')
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(out_path, fourcc, 20.0,
(int(cap.get(3)), int(cap.get(4))))
for pos in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):
ret, frame = cap.read()
datum.cvInputData = frame
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
out.write(datum.cvOutputData)
print(datum.poseKeypoints)
print(cap.get(cv2.CAP_PROP_POS_FRAMES))
cap.release()
print('end')
except Exception as e:
print(dir(e))
print(e)
sys.exit(-1)
# if file.filename == '':
# flash('No selected file')
# return redirect(request.url)
# if file and allowed_file(file.filename):
# filename = secure_filename(file.filename)
# file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
# return redirect(url_for('upload_file',
# filename=filename))
# return '''
# <!doctype html>
# <title>Upload new File</title>
# <h1>Upload new File</h1>
# <form method=post enctype=multipart/form-data>
# <input type=file name=file>
# <input type=submit value=Upload>
# </form>
# '''
return "good"
if request.method == 'GET':
return '''
def process_picture(picture_path):
try:
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if sys.platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Flags
parser = argparse.ArgumentParser()
png_path = "../../../examples/media/traffic_police_gesture/" + picture_path[
79:]
#parser.add_argument("--image_path", default="../../../examples/media/COCO_val2014_000000000192.jpg", help="Process an image. Read all standard formats (jpg, png, bmp, etc.).")
parser.add_argument(
"--image_path",
default=png_path,
help=
"Process an image. Read all standard formats (jpg, png, bmp, etc.)."
)
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1: next_item = args[1][i + 1]
else: next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
# Construct it from system arguments
# op.init_argv(args[1])
# oppython = op.OpenposePython()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
imageToProcess = cv2.imread(args[0].image_path)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
# Display Image
path = '/Users/zephyryau/Documents/study/INF552/Project/input_picture/'
with open(path + 'result.txt', 'w', newline='') as txtfile:
spamwriter = csv.writer(txtfile)
spamwriter.writerow(datum.poseKeypoints)
#print("Body keypoints: \n" + datum.poseKeypoints)
cv2.imwrite(path + 'result.png', datum.cvOutputData)
cv2.waitKey(0)
except Exception as e:
print(e)
sys.exit(-1)
def __init__(self, opt):
self.session_id = opt.session_id
# Initialize camera and window
self.cap = cv.VideoCapture(opt.input_device,
eval(f'cv.{opt.input_api}'))
assert self.cap.isOpened(), 'Failed to initialize video capture!'
self.width, self.height = opt.video_width, opt.video_height
self.cap.set(cv.CAP_PROP_FRAME_WIDTH, self.width)
self.cap.set(cv.CAP_PROP_FRAME_HEIGHT, self.height)
self.window_name = 'tutorial'
if opt.fullscreen:
cv.namedWindow(self.window_name, cv.WINDOW_NORMAL)
cv.setWindowProperty(self.window_name, cv.WND_PROP_FULLSCREEN,
cv.WINDOW_FULLSCREEN)
else:
cv.namedWindow(self.window_name)
self.width_qr = self.width // 4
self.width_half = self.width // 2
self.width_3qr = self.width // 4 * 3
self.border_margin = round(max(self.width, self.height) * 0.025)
# Initialize openpose library
import os
import sys
sys.path.append(os.fspath(opt.op_path / 'python/openpose/Release'))
os.environ['PATH'] += f';{os.fspath(opt.op_path/"x64/Release")}'
os.environ['PATH'] += f';{os.fspath(opt.op_path/"bin")}'
try:
import pyopenpose as op
except ImportError as e:
print('Error: OpenPose library could not be found.')
raise e
self._op_wrapper = op.WrapperPython()
self._op_wrapper.configure({
'model_folder':
os.fspath(opt.op_path / '../models/'),
'model_pose':
'BODY_25',
'number_people_max':
1
})
self._op_wrapper.start()
self._datum = op.Datum()
# Raw data recorder
if not opt.no_save:
self.recorder = DataRecorder(
(self.width, self.height), 24,
f'records/{opt.session_id}/{int(time())}')
else:
self.recorder = DummyDataRecorder()
# Handler of each state
self.handlers = {
State.Ready: self.handle_ready,
State.PoseReady: self.handle_pose_ready,
State.PoseMeasuring: self.handle_pose_measuring,
State.Finish: self.handle_finish,
}
# Load assets
self.assets = Assets('assets/')
self.poses: List[Posture] = [
Pose_01, Pose_02, Pose_03, Pose_04, Pose_05, Pose_06, Pose_08,
Pose_09, Pose_10
]
self.poses_ui: List[InformationLayer] = generate_info_layers(
self.poses)
self.running: bool = True
self.state: State = State.Ready
self.pose_index_iter: Iterator[int] = iter(range(len(self.poses)))
self.current_pose_i: int = 0
self.t_start: float = perf_counter()
self.fail_counter = deque([False] * opt.fail_tolerance,
maxlen=opt.fail_tolerance)
self.angle: float = np.pi
self.confidence: float = 0
self.score: int = 0
self.keypoints: np.ndarray = np.array([], np.float32)
self.frame: np.ndarray = np.array([], np.uint8)
def initialize(image, identity):
try:
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('/home/ubuntu/openpose/build/python')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('/home/ubuntu/openpose/build/python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "/home/ubuntu/openpose/models/"
params["face"] = True
params["hand"] = False
params["number_people_max"] = 1
#params["write_json"] = "/home/ubuntu/server/tmp/"
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
imageToProcess = cv2.imread(image)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
nose = datum.poseKeypoints[0][0]
chest = datum.poseKeypoints[0][1]
left_shoulder = datum.poseKeypoints[0][2]
right_shoulder = datum.poseKeypoints[0][5]
data = {}
data['quantitative'] = []
data['quantitative'].append({
'shoulder_width':
str(distance(left_shoulder, right_shoulder)),
'shoulder_slope':
str(slope(right_shoulder, left_shoulder)),
'head_slope':
str(slope(nose, chest)),
'left_shoulder_neck':
str(slope(chest, left_shoulder)),
'right_shoulder_neck':
str(slope(chest, right_shoulder)),
'eye_distance':
str(
distance(datum.faceKeypoints[0][36],
datum.faceKeypoints[0][45]))
})
print("Done " + image)
return data
except (Exception, Warning) as e:
print("Initial IMAGE: " + e)
def image_preprocess(file_path):
try:
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Flags
parser = argparse.ArgumentParser()
parser.add_argument(
"--image_path",
default=file_path,
help=
"Process an image. Read all standard formats (jpg, png, bmp, etc.)."
)
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
params["write_images"] = "./src/temp"
params["write_images_format"] = "png"
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1:
next_item = args[1][i + 1]
else:
next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
imageToProcess = cv2.imread(args[0].image_path)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
# Calculate Angles
data = {
'kneeHipAngle':
calculate_angle(datum.poseKeypoints[0][12],
datum.poseKeypoints[0][13],
datum.poseKeypoints[0][14]),
'hipChestAngle':
calculate_angle(datum.poseKeypoints[0][5],
datum.poseKeypoints[0][12],
datum.poseKeypoints[0][13]),
'chestArmAngle':
calculate_angle(datum.poseKeypoints[0][12],
datum.poseKeypoints[0][5],
datum.poseKeypoints[0][6]),
'armAngleDiff':
calculate_angle(datum.poseKeypoints[0][4],
datum.poseKeypoints[0][5],
datum.poseKeypoints[0][7]),
'kneeAngleDiff':
calculate_angle(datum.poseKeypoints[0][11],
datum.poseKeypoints[0][13],
datum.poseKeypoints[0][14])
}
return data
except Exception as e:
print(e)
sys.exit(-1)
def body_detect(video):
try:
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('/home/daniel/openpose/build/python/');
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
# este import esta a estragar o celery
import pyopenpose as op
except ImportError as e:
print(e)
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
raise e
# Flags
parser = argparse.ArgumentParser()
parser.add_argument("--video", default="/home/daniel/uni/CheckMeWOD/checkmewod_project/"+video, help="Process a video.")
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = '/home/daniel/openpose/models/'
params["face"] = False
params["hand"] = False
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1:
next_item = args[1][i + 1]
else:
next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
# Construct it from system arguments
# op.init_argv(args[1])
# oppython = op.OpenposePython()
# Starting OpenPose
keypointlist = []
keypointdict = {}
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process video
datum = op.Datum()
frame_num = 0
cap = cv2.VideoCapture(args[0].video)
while (cap.isOpened()):
hasframe, frame = cap.read()
if hasframe == True:
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
# Display video
keypointdict['body keypoint'] = np.array(datum.poseKeypoints).tolist()
keypointlist.append(keypointdict.copy()) # must be the copy!!!
#cv2.imshow("OpenPose 1.5.0 - Tutorial Python API", datum.cvOutputData)
filename = "/home/daniel/uni/CheckMeWOD/checkmewod_project/checkmewod/video_evaluation_src/output_json/frame_number_" + str(frame_num) + ".json"
with open(filename, "a") as f:
json.dump(keypointlist, f, indent=0)
frame_num += 1
cv2.waitKey(1)
else:
break
except Exception as e:
# print(e)
sys.exit(-1)
def toCsv(file_path):
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release')
os.environ['PATH'] = os.environ['PATH'] + ';' + dir_path + \
'/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('/home/aleix/openpose/build/python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print('Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
raise e
# Flags
parser = argparse.ArgumentParser()
parser.add_argument("--video_path", default=file_path,
help="Process an image. Read all standard formats (jpg, png, bmp, etc.).")
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "/home/aleix/openpose/models/"
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1])-1:
next_item = args[1][i+1]
else:
next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params:
params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params:
params[key] = next_item
try:
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
imageToProcess = cv2.VideoCapture(args[0].video_path)
arrayNoNorm = np.array(0)
totalFrames = int(imageToProcess.get(cv2.CAP_PROP_FRAME_COUNT))
target = cutFps(totalFrames)
counter = 0
framesRet = 0
# 1. Guardem totes les posicions del vídeo en una array.
while(imageToProcess.isOpened()):
if counter == target:
ret, frame = imageToProcess.read()
if ret == True:
datum.cvInputData = frame
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
# Display Image
kp_array = np.array(datum.poseKeypoints)
# Treiem els outliers
nou_array = np.delete(kp_array[0], [3, 4, 15, 17, 22, 23], 0)
arrayNoNorm = np.append(arrayNoNorm, nou_array)
# print("Body keypoints: \n" + str(datum.poseKeypoints))
# cv2.imshow("OpenPose 1.7.0 - Tutorial Python API",
# datum.cvOutputData)
cv2.waitKey(1)
counter = 0
framesRet += 1
else:
break
else:
imageToProcess.grab()
counter += 1
label = file_path.split('/')[7]
label = label.split('_')[0]
label = label.split('.')[0]
print(label)
#label = label.join([c for c in label if c.isalpha()])
pos = 0
# print("Frames sencers: ",totalFrames)
# print("Frames retallats: ", framesRet)
for i in range(framesRet):
# 2. Normalitzem dades frame per frame (cada 75 posicions ja que te x, y i accuracy)
posini = pos
pos += 57
arrayFrame = arrayNoNorm[posini:pos]
# print(len(arrayFrame))
# if len(arrayFrame) == 1:
# print(arrayFrame)
arrayFrame = normValues(arrayFrame, label)
# 3. Les guardem en un csv
with open('/home/aleix/Escriptori/coses_tfg/tfg-correct-exercise/api/files/keypoints.csv', 'a') as file:
writer = csv.writer(file)
writer.writerow(arrayFrame)
# Fem padding fins a 75, ja que és el màxim de frames.
padding = [label]
for i in range(38):
padding.append(-1000.0)
while(framesRet != 75):
with open('/home/aleix/Escriptori/coses_tfg/tfg-correct-exercise/api/files/keypoints.csv', 'a') as file:
writer = csv.writer(file)
writer.writerow(padding)
framesRet += 1
except Exception as e:
print(e)
sys.exit(-1)
def main():
dir_path = 'C:\\Users\\SignEasy\\Documents\\openpose-1.5.0'
sys.path.append("{}\\build\\python\\openpose\\Release".format(dir_path))
os.environ["PATH"] = os.environ[
"PATH"] + ";{}\\build\\x64\\Release;{}\\build\\bin;{};".format(
dir_path, dir_path, dir_path)
try:
import pyopenpose as op
except ImportError as e:
print(e, file=sys.stderr)
# OpenPose Flags
parser = argparse.ArgumentParser()
parser.add_argument("--hand", default=True)
parser.add_argument("--display", default=0)
parser.add_argument("--number_people_max", default=1)
args = parser.parse_known_args()
# OpenPose Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "{}\\models".format(dir_path)
params["hand"] = True
params["display"] = 0
params["number_people_max"] = 1
# Add others command line arguments
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1:
next_item = args[1][i + 1]
else:
next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
try:
import tensorflow as tf
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
datum = op.Datum()
# Start TensorFlow, load Keras model
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.1 # Only allocates a portion of VRAM to TensorFlow
session = tf.Session(config=config)
# TODO CHANGE WITH EVERY NEW MODEL
tf_model = load_model(
'Models/dynamic_kfold-7_model_date_04-23-2021_time_18-51-49.h5'
) # 'normalized_epochs200_10_data_points10_06_2019_02_00_54.h5
# Capture Frames
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW) # Use webcam
num_data_points = 0
frame_counter = 0
result = ""
xy_set = []
conf_level_sum = 0
while True:
ret, frame = cap.read()
if not ret:
break
# Use Webcam frames, render OpenPose
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
op_frame = datum.cvOutputData
window_name = "Hand Classification Window"
# All available hand keypoints (OpenPose 1.5 (0-20))
hand_data = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20
]
# TODO Add new gestures to this
prediction_strings = ["Hello", "Thank You", "A", "W"]
x_set = []
y_set = []
num_predictions = len(prediction_strings)
# Ensure hand keypoints exist before doing classification
try:
if 0 <= frame_counter < 20:
rightHandKeypoints = datum.handKeypoints[1]
for entries in rightHandKeypoints:
for hand_entry in hand_data:
conf_level_sum += entries[hand_entry][2]
x_set.append(entries[hand_entry][0])
y_set.append(entries[hand_entry][1])
xy_set = xy_set + x_set
xy_set = xy_set + y_set
bottom_left = "Frame: " + str(frame_counter)
cv2.putText(op_frame,
bottom_left, (10, 100),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(0, 0, 0),
thickness=2)
frame_counter += 1
elif frame_counter == 20:
xy_set = np.asarray(xy_set, dtype=np.float32)
xy_set = xy_set.reshape(1, -1)
transformer = Normalizer().fit(xy_set)
X_test = transformer.transform(xy_set)
# Prediction occurs here
predictions = tf_model.predict(xy_set)
predictions = predictions.flatten()
# Issue here is that conf_level_sum works until last iteration
print("conf_level_sum: " + str(conf_level_sum))
conf_level = conf_level_sum / num_predictions / 10
print("conf_level: " + str(conf_level))
if conf_level > .85:
predictionToDisplay = prediction_strings[np.argmax(
predictions)]
else:
predictionToDisplay = "N/A"
print("prediction: " + str(predictionToDisplay))
conf_level_sum = 0
frame_counter += 1
elif 21 <= frame_counter < 50:
# Show prediction
cv2.putText(op_frame,
"Result: " + predictionToDisplay, (10, 100),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(0, 0, 0),
thickness=2)
frame_counter += 1
elif frame_counter == 50:
frame_counter = 0
xy_set = []
# clear prediction
except Exception as e:
cv2.putText(op_frame,
"Restarting, please put hand in frame.", (10, 100),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(0, 0, 0),
thickness=2)
print(e)
conf_level_sum = 0
frame_counter = 0
xy_set = []
pass
cv2.namedWindow(window_name, cv2.WND_PROP_FULLSCREEN)
cv2.imshow(window_name, op_frame)
if cv2.waitKey(1) == ord('q'):
cap.release()
cv2.destroyAllWindows()
print("Terminating Program")
exit()
except Exception as e:
print(e)
sys.exit(-1)
def main():
global speed, steeringAngle, status, last_status, selected_direction, RightWirst_y, RightWirst_x, LeftWirst_y, LeftWirst_x, carSerial, accelerations, steering_angles, alpha, MAX_OP_MULTIPLIER, FLIP_STEER
# Starting serial bluetooth connection
if sendCommandsToCar:
carSerial = ConnectToSerial()
if carSerial is None:
print("No port selected, exiting...")
sys.exit(-2)
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Start webcam with VideoCapture. 0 -> use default webcam
# WINDOWS_NORMAL dynamic window resize at running time
# resizeWindow output windows webcam dimension. RESOLUSpeedsTION -> 4:3
cv2.namedWindow('OPtoROBO', cv2.WINDOW_NORMAL)
cv2.resizeWindow('OPtoROBO', 1000, 750)
stream = cv2.VideoCapture(source)
time.sleep(2)
if stream is None:
print("No stream at " + str(source))
sys.exit(-3)
if not stream.isOpened():
print("Stream at " + str(source) + " unvailable. Unable to open.")
sys.exit(-4)
# Frame counter
counter = 0
# Execution time
start = time.time()
fps = 0.0
fps_last_time = time.time() - 1000
fps_last_counter = counter - 1
quit_count = 0
error_op_counter = 0
# moving average filter
ret, img = stream.read()
avg = np.float32(img)
while True:
# Update fps
if counter % 5 == 1:
fps = (counter - fps_last_counter) / (time.time() - fps_last_time)
fps_last_time = time.time()
fps_last_counter = counter
# Frame reader. Each frame will be processed by OpenPose
ret, img = stream.read()
# Output flip
cv2.flip(img, 1, img)
# Bilateral Filtering
img = cv2.bilateralFilter(img, 9, 40, 75)
datum = op.Datum()
datum.cvInputData = img
opWrapper.emplaceAndPop([datum])
# Detecting people control
if (not datumChecks(datum.poseKeypoints) or str(datum.poseKeypoints) == str(2.0) or str(
datum.poseKeypoints) == str(0.0)
or str(datum.poseKeypoints) == str(1e-45)):
print('NO DETECTED PEOPLE, YOU SHOULD GO IN FRONT OF YOUR WEBCAM')
# time.sleep(.5)
error_op_counter += 1
if error_op_counter > (int(fps / MAX_OP_MULTIPLIER)):
RightWirst_x.append(400.0)
RightWirst_y.append(440.0)
LeftWirst_x.append(250.0)
LeftWirst_y.append(440.0)
else:
RightWirst_x.append(RightWirst_x[len(RightWirst_y) - 1])
RightWirst_y.append(RightWirst_y[len(RightWirst_y) - 1])
LeftWirst_x.append(LeftWirst_x[len(LeftWirst_x) - 1])
LeftWirst_y.append(LeftWirst_x[len(LeftWirst_x) - 1])
else:
error_op_counter = 0
try:
RightWirst_x.append(datum.poseKeypoints[0][7][0])
RightWirst_y.append(datum.poseKeypoints[0][7][1])
LeftWirst_x.append(datum.poseKeypoints[0][4][0])
LeftWirst_y.append(datum.poseKeypoints[0][4][1])
except Exception as e1:
print(e1)
c = 0
steeringAngle = steering_angle(LeftWirst_x[counter], -LeftWirst_y[counter],
RightWirst_x[counter], -RightWirst_y[counter])
# Direction and Stop
# if both hands up
if (LeftWirst_y[counter] < 380 or RightWirst_y[counter] < 380) and selected_direction != 0:
# Go time
status = 1
else:
# if one or both hands down into command part
status = 0
Stop()
if (380 < LeftWirst_y[counter] < 480 and 0 < LeftWirst_x[counter] < 160
and RightWirst_y[counter] > 380):
selected_direction = 1
print('<-------BACKWARD', status)
elif (380 < RightWirst_y[counter] < 480 and 480 < RightWirst_x[counter] < 640
and LeftWirst_y[counter] > 380):
selected_direction = 2
print('FORWARD-------->', status)
elif (((LeftWirst_y[counter] > 380.0 and RightWirst_y[counter] > 380.0) or (
LeftWirst_y[counter] == 0.0
and RightWirst_y[
counter] == 0.0) or
(LeftWirst_y[counter] == 0.0 and RightWirst_y[counter] > 380.0)
or (LeftWirst_y[counter] > 380.0 and RightWirst_y[counter] == 0.0))
and (160 < LeftWirst_x[counter] < 640 and 0 < RightWirst_x[counter] < 480)
or (LeftWirst_x[counter] == 0.0 and RightWirst_x[counter] == 0.0)):
speed = 0
Stop()
print('------STOP------', status)
# If we just exited from stop zone, a Forward of Backward call is needed
if status == 1 and last_status == 0:
if selected_direction == 1:
Backward()
elif selected_direction == 2:
Forward()
# if last_status == 1 and status == 0:
# selected_direction = 0
# Gestures detection
if status == 1:
speed = int(speed_value(RightWirst_y[counter], LeftWirst_y[counter]))
if speed < 0:
speed = 0
if (min_angle_front < steeringAngle < max_angle_front and RightWirst_y[counter] < 380.0 and LeftWirst_y[
counter] < 380.0):
print('----FRONT----. STATUspeedMS: ', status_to_str(), '. SPEED: ', speed, '. ANGLE: ', 0)
sendSpeed()
else:
if (max_angle_front < steeringAngle < 90.0 and RightWirst_y[counter] < 380.0
and LeftWirst_y[counter] < 380.0):
print('LEFT---------. STATUS: ', status_to_str(), '. SPEED: ', speed, '. ANGLE: ',
round(steeringAngle, 2))
sendSpeed()
if FLIP_STEER:
steeringAngle = -steeringAngle
Steer(FLIP_STEER)
else:
if (-90.0 < steeringAngle < min_angle_front and RightWirst_y[counter] < 380.0
and LeftWirst_y[counter] < 380.0):
print('--------RIGHT. STATUS: ', status_to_str(), '. SPEED: ', speed, '. ANGLE: ',
round(steeringAngle, 2))
sendSpeed()
if FLIP_STEER:
steeringAngle = -steeringAngle
Steer(FLIP_STEER)
# Output with OpenPose skeleton
img2 = datum.cvOutputData
# Show line between hands
steer_color = steer_front
if steeringAngle < min_angle_front:
steer_color = steer_left
if steeringAngle > max_angle_front:
steer_color = steer_right
if not RightWirst_x[counter] == 0 and not LeftWirst_x[counter] == 0:
cv2.line(img2, pt1=(int(RightWirst_x[counter]), int(RightWirst_y[counter])),
pt2=(int(LeftWirst_x[counter]), int(LeftWirst_y[counter])), color=steer_color, thickness=5)
# Stop Line
cv2.line(img2, (160, 380), (480, 380), (0, 0, 255), thickness=3)
cv2.putText(img2, 'STOP', (260, 420), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (0, 0, 255), thickness=2)
cv2.putText(img2, 'B', (65, 420), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (0, 255, 0), thickness=2)
cv2.putText(img2, 'F', (545, 420), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (255, 0, 0), thickness=2)
# Show speedometer
cv2.putText(img2, "SPD: " + str(speed), (right_UI_status_X, 30), cv2.FONT_HERSHEY_TRIPLEX, .5, (0, 0, 0),
thickness=2)
# Show Speed ui
cv2.rectangle(img2, (610, (370 - speed)), (630, 370), (0, 255, 0), thickness=-2)
# Show steerAngle
cv2.putText(img2, "STR: " + str(int(steeringAngle)), (right_UI_status_X, 60), cv2.FONT_HERSHEY_TRIPLEX, .5,
(0, 0, 0),
thickness=2)
# Show Fps
cv2.putText(img2, "FPS: " + str(round(fps, 1)), (right_UI_status_X, 90), cv2.FONT_HERSHEY_TRIPLEX, .5,
(0, 0, 0),
thickness=2)
# Show Mode
if status == 0:
cv2.putText(img2, 'STOP MODE', (20, 30), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (0, 0, 255), thickness=2)
if selected_direction == 1:
cv2.putText(img2, 'BACKWARD', (170, 30), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (255, 0, 0), thickness=2)
elif selected_direction == 2:
cv2.putText(img2, 'FORWARD', (170, 30), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (255, 0, 0), thickness=2)
elif status == 1:
cv2.putText(img2, 'GO MODE', (20, 30), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (0, 255, 0), thickness=2)
if selected_direction == 1:
cv2.putText(img2, 'BACKWARD', (170, 30), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (255, 0, 0), thickness=2)
elif selected_direction == 2:
cv2.putText(img2, 'FORWARD', (170, 30), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (255, 0, 0), thickness=2)
# Quitting progress bar
if quit_count != 0:
cv2.putText(img2, ' Quitting...', (10, 60), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (0, 255, 0), thickness=2)
cv2.rectangle(img2, (22, 70), (122, 90), (0, 255, 0), thickness=-2)
cv2.rectangle(img2, (22 + 10 * quit_count, 70), (122, 90), (255, 255, 255), thickness=-2)
# Backward/Forward zones
cv2.rectangle(img2, (0, 380), (160, 480), (0, 255, 0), thickness=2)
cv2.rectangle(img2, (480, 380), (640, 480), (255, 0, 0), thickness=2)
img2 = imshow_img(img2, avg, alpha)
cv2.imshow('OPtoROBO', img2)
if speed >= 370:
c = 0
counter = counter + 1
last_status = status
# Quit gesture
try:
if (380 < LeftWirst_y[counter] < 480 and 0 < LeftWirst_x[counter] < 160
and 380 < RightWirst_y[counter] < 480 and 480 < RightWirst_x[counter] < 640):
cv2.rectangle(img, (160, 400), (480, 420), (0, 255, 0), thickness=2)
quit_count = quit_count + 1
if quit_count > 10:
break
else:
quit_count = 0
except Exception as e1:
print(e1)
c = 0
# q, Q == quit, STOP SCRIPT; NB: waitKey MUST BE 1
key = cv2.waitKey(1)
if key == ord('q') or key == ord('Q'):
break
end = time.time()
# Resources release
stream.release()
cv2.destroyAllWindows()
total_time = end - start
# Time and fps
print(
'-----------------------------------------------------------------------------------------------------------')
print('Total script execution time : ', total_time)
print('FPS: ', counter / total_time)
print(
'-----------------------------------------------------------------------------------------------------------')
def pose_estimation(self):
# load openpose python api
if self.arg.openpose is not None:
# sys.path.append('{}/python'.format(self.arg.openpose))
# sys.path.append('{}/build/python'.format(self.arg.openpose))
# print(self.arg.openpose + '/build/python/openpose/Release')
sys.path.append(self.arg.openpose +
'/build/python/openpose/Release')
# sys.path.append(self.arg.openpose + '\\build\\x64\\Release')
# sys.path.append(self.arg.openpose + '\\build\\bin')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + self.arg.openpose + '/build/x64/Release;' + self.arg.openpose + '/build/bin;'
print(sys.path)
try:
# from openpose import pyopenpose as op
import pyopenpose as op
except:
print('Can not find Openpose Python API.')
return
video_name = self.arg.video.split('/')[-1].split('.')[0]
# initiate
opWrapper = op.WrapperPython()
params = dict(model_folder='./models', model_pose='COCO')
opWrapper.configure(params)
opWrapper.start()
self.model.eval()
video_capture = cv2.VideoCapture(self.arg.video)
video_length = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
pose_tracker = naive_pose_tracker(data_frame=video_length)
# pose estimation
start_time = time.time()
frame_index = 0
video = list()
pose_list = []
pbar = tqdm.tqdm(total=video_length)
while (True):
# get image
ret, orig_image = video_capture.read()
if orig_image is None:
break
source_H, source_W, _ = orig_image.shape
orig_image = cv2.resize(orig_image,
(self.arg.shortedge, self.arg.shortedge *
source_H // source_W))
H, W, _ = orig_image.shape
video.append(orig_image)
# pose estimation
datum = op.Datum()
datum.cvInputData = orig_image
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
multi_pose = datum.poseKeypoints # (num_person, num_joint, 3)
if len(multi_pose.shape) != 3:
continue
my_pose = multi_pose.copy()
my_pose = self.__get_prior_person(my_pose)
pose_list.append(my_pose)
# normalization
multi_pose[:, :, 0] = multi_pose[:, :, 0] / W
multi_pose[:, :, 1] = multi_pose[:, :, 1] / H
multi_pose[:, :, 0:2] = multi_pose[:, :, 0:2] - 0.5
multi_pose[:, :, 0][multi_pose[:, :, 2] == 0] = 0
multi_pose[:, :, 1][multi_pose[:, :, 2] == 0] = 0
# pose tracking
pose_tracker.update(multi_pose, frame_index)
frame_index += 1
pbar.update(1)
# print('Pose estimation ({}/{}).'.format(frame_index, video_length))
data_numpy = pose_tracker.get_skeleton_sequence()
pbar.close()
# np.save('stgcn_data_numpy_original.npy', data_numpy)
return video, data_numpy, pose_list
def findPose(videoPath, resultsPath):
try:
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release');
os.environ['PATH'] = os.environ['PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python');
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print('Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
raise e
# Flags
parser = argparse.ArgumentParser()
# parser.add_argument("--image_path", default="../../../examples/media/COCO_val2014_000000000428.jpg", help="Process an image. Read all standard formats (jpg, png, bmp, etc.).")
parser.add_argument("--video", default=videoPath)
# parser.add_argument("--number_people_max", default = "2")
# parser.add_argument("--write_video", default= "testResults.avi")
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
params["number_people_max"] = 2
params["write_images"] = resultsPath
# params["write_video"] = "testResults.avi"
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1])-1: next_item = args[1][i+1]
else: next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-','')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-','')
if key not in params: params[key] = next_item
print("args", args)
print("params",params)
#Construct it from system arguments
# op.init_argv(args[1])
# oppython = op.OpenposePython()
#Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Video
datum = op.Datum()
capture = cv2.VideoCapture(args[0].video)
fps = capture.get(cv2.CAP_PROP_FPS)
frames = []
print('fps: ', fps)
i = 0
count = 0
while(capture.isOpened()):
ret, frame = capture.read()
if ret == False:
break
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
keypoints = datum.poseKeypoints
frame = buildFrame25(keypoints, i)
frames.append(frame)
# print("Body keypoints: \n" + str(keypoints))
cv2.imwrite(resultsPath +'/video'+str(i)+'.jpg', datum.cvOutputData)
# cv2.waitKey(20)
# cv2.imwrite('video'+str(i)+'.jpg',frame)
i+=1
count+=int(fps)
capture.set(1, count)
# if count >= 100:
# break
capture.release()
return frames
except Exception as e:
print(e)
sys.exit(-1)
def main():
try:
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
# from openpose import *
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
# from openpose import *
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
params["hand"] = True
params["hand_detector"] = 2
params["body"] = 1
params["net_resolution"] = '320x192' #20*11
# params["face"] = True
# params["disable_blending"] = True
# params["fps_max"] = 5
handRectangles = [[
op.Rectangle(128, 0, 1024, 1024),
op.Rectangle(0., 0., 0., 0.)
]]
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
datum.handRectangles = handRectangles
cam = cv2.VideoCapture(0) # modify here for camera number
cam.set(3, 1280)
cam.set(4, 1024)
pair_poseKeypoints = [[], []]
input_hands = []
prev_state = None
msg_state = ('not_sent', time.perf_counter())
while (cv2.waitKey(1) != 27):
if msg_state[0] == 'sent':
if time.perf_counter() - msg_state[1] > 2.5:
msg_state = ('not_sent', time.perf_counter())
ret, frame = cam.read()
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
frame = datum.cvOutputData
'''If Person not in Camera'''
if datum.poseKeypoints.shape == ():
# conn.request("POST", "/v2/chat/users/[email protected]/messages", payload, headers)
#
# res = conn.getresponse()
# data = res.read()
#
# print(data.decode("utf-8"))
if msg_state[0] == 'not_sent':
# print('WHY NOT WORKING')
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_absent, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 3
fontColor = (255, 255, 0)
lineType = 2
fontThickness = 2
msg_on_screen = 'ABSENT!'
textsize = cv2.getTextSize(msg_on_screen, font, fontScale,
fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
cv2.rectangle(frame, (0, 0), (1280, 1024), (0, 0, 255), 20)
cv2.putText(frame, 'Absent!', bottomLeftCornerOfText, font,
fontScale, fontColor, lineType)
cv2.imshow("Openpose 1.4.0 Webcam", frame)
continue
'''Evaluate Movement & Confidence'''
del pair_poseKeypoints[0]
pair_poseKeypoints.append(datum.poseKeypoints[0])
body_confidence_avg = avg_pose_confidence(datum.poseKeypoints[0])
# print(body_confidence_avg)
moved = metric(pair_poseKeypoints)
'''Evaluate Hand Gesture'''
if len(input_hands) == 12:
del input_hands[0]
input_hands.append(datum.handKeypoints[0][0])
# print(len(input_hands))
prob, gesture = None, None
hand_confidence_avg = avg_list_confidence(input_hands)
# if len(input_hands) == 12 and avg >= 0.1:
if len(input_hands) == 12:
# print('Confidence : ', hand_confidence_avg)
prob, gesture = get_hand_gesture('normalizev2.pt', input_hands,
'cuda')
# print(prob, gesture)
'''Output Recognition Results'''
print_msg = False
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = None
fontColor = None
fontScale = 3
fontThickness = 2
msg_on_screen = None
if valid_hand(hand_confidence_avg, gesture) and gesture == 1:
print('THUMBS DOWN PROB : ', prob)
if prob > 11:
'''Counter'''
if prev_state is None:
prev_state = ('thumbs_down', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 5.5:
prev_state = ('thumbs_down', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'thumbs_down':
prev_state = ('thumbs_down', time.perf_counter())
# print(prev_state)
else:
# print(time.perf_counter() - prev_state[1])
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_thumbsdown, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
if time.perf_counter() - prev_state[1] > 0.5:
print_msg = True
# bottomLeftCornerOfText = (450, 500)
fontColor = (255, 0, 0)
fontScale = 3
msg_on_screen = 'THUMBS DOWN'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale,
fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) //
2,
(1024 + textsize[1]) //
2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
elif valid_hand(hand_confidence_avg, gesture) and gesture == 2:
print('THUMBS DOWN PROB : ', prob)
'''Counter'''
if prob > 11:
if prev_state is None:
prev_state = ('thumbs up', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 5.5:
prev_state = ('thumbs_up', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'thumbs_up':
prev_state = ('thumbs_up', time.perf_counter())
# print(prev_state)
else:
# print(time.perf_counter() - prev_state[1])
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_thumbsup, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
if time.perf_counter() - prev_state[1] > 0.5:
print_msg = True
# bottomLeftCornerOfText = (450, 500)
fontColor = (0, 255, 0)
fontScale = 3
msg_on_screen = 'THUMBS UP'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale,
fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) //
2,
(1024 + textsize[1]) //
2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
elif valid_hand(hand_confidence_avg, gesture) and gesture == 4:
print('RAISE HAND PROB : ', prob)
'''Counter'''
if prev_state is None:
prev_state = ('raise_hand', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 5.5:
prev_state = ('raise_hand', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'raise_hand':
prev_state = ('raise_hand', time.perf_counter())
# print(prev_state)
else:
# print(time.perf_counter() - prev_state[1])
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_raisehand, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
if time.perf_counter() - prev_state[1] > 0.5:
print_msg = True
bottomLeftCornerOfText = (450, 500)
fontColor = (0, 255, 255)
fontScale = 3
msg_on_screen = 'HAND RAISED'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale, fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
elif moved:
'''Counter'''
if prev_state is None:
prev_state = ('detect_move', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 1.5:
prev_state = ('detect_move', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'detect_move':
prev_state = ('detect_move', time.perf_counter())
# print(prev_state)
else:
# print(msg_state)
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_movement, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
print_msg = True
bottomLeftCornerOfText = (150, 500)
fontColor = (255, 255, 255)
fontScale = 3
msg_on_screen = 'MOVEMENT DETECTED'
textsize = cv2.getTextSize(msg_on_screen, font, fontScale,
fontThickness)[0]
# print(textsize)
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
if print_msg:
# print(data.decode("utf-8"))
# bottomLeftCornerOfText = (550, 500)
# fontScale = 2
# fontColor = (255, 0, 0)
lineType = 2
cv2.rectangle(frame, (0, 0), (1280, 1024), fontColor, 40)
cv2.putText(frame, msg_on_screen, bottomLeftCornerOfText, font,
fontScale, fontColor, lineType)
cv2.imshow("Openpose 1.4.0 Webcam", frame) # datum.cvOutputData
# Always clean up
cam.release()
cv2.destroyAllWindows()
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
print(e)
sys.exit(-1)
def run_video(video,
path='',
skip_cog=False,
skip_track=False,
plt_graph=False,
cog_color='black',
start_frame=0,
debug=False):
try:
logging.shutdown()
reload(logging)
except Exception as e:
raise e
logger = getLogger("APP_LOG")
stream_handler = StreamHandler()
if debug:
logger.setLevel(logging.DEBUG)
stream_handler.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
stream_handler.setLevel(logging.INFO)
handler_format = Formatter('%(name)s, %(levelname)s:\t%(message)s')
stream_handler.setFormatter(handler_format)
logger.addHandler(stream_handler)
# setting directories to output
path_movie_out = os.path.join(path, 'movies_estimated')
path_csv_estimated = os.path.join(path, 'data_estimated')
path_png_estimated = os.path.join(path, 'png_estimated')
csv_file = os.path.join(path_csv_estimated, video.rsplit('.')[0] + '.csv')
os.makedirs(path_movie_out, exist_ok=True)
os.makedirs(path_png_estimated, exist_ok=True)
os.makedirs(path_csv_estimated, exist_ok=True)
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# open video
cap = cv2.VideoCapture(os.path.join(path, 'movies', video))
logger.info("OPEN: %s" % video)
if cap.isOpened() is False:
logger.info("ERROR: opening video stream or file")
caps_fps = cap.get(cv2.CAP_PROP_FPS)
ma = MotionAnalysis()
# CSV FILE SETTING
segments = [
"Nose", "Neck", "RShoulder", "RElbow", "RWrist", "LShoulder", "LElbow",
"LWrist", "MidHip", "RHip", "RKnee", "RAnkle", "LHip", "LKnee",
"LAnkle", "background", "REye", "LEye", "REar", "LEar", "LBigToe",
"LSmallToe", "LHeel", "RBigToe", "RSmallToe", "RHeel", "head_cog",
"torso_cog", "r_thigh_cog", "l_thigh_cog", "r_leg_cog", "l_leg_cog",
"r_foot_cog", "l_foot_cog", "r_arm_cog", "l_arm_cog", "r_forearm_cog",
"l_forearm_cog", "r_hand_cog", "l_hand_cog"
]
seg_columns = ['frame', 'human_id']
[seg_columns.extend([x + '_x', x + '_y', x + '_score']) for x in segments]
df_template = pd.DataFrame(columns=seg_columns)
df_template.to_csv(csv_file, index=False)
# processing video
frame_no = 0
while cap.isOpened():
ret_val, image = cap.read()
if not ret_val:
break
if frame_no == 0:
h_pxl, w_pxl = image.shape[0], image.shape[1]
# estimate pose
t = time.time()
datum = op.Datum()
datum.cvInputData = image
opWrapper.emplaceAndPop([datum])
time_estimation = time.time() - t
# keypoints
humans = datum.poseKeypoints
# calculate and save data
t = time.time()
ma.track_humans(frame_no, humans)
# calculate cog
bodies_cog = ma.bodies_cog
# bodies_cog[np.isnan(bodies_cog[:, :, :])] = 0 # to plot, fill nan
df_frame = pd.DataFrame(ma.humans_current.round(1))
df_frame.to_csv(csv_file, index=False, header=None, mode='a')
time_cog = time.time() - t
# logging to check progress and speed
if frame_no % int(caps_fps) == 0:
logger.info('calculation of cog in %.4f seconds.' % time_cog)
logger.info("Now estimating at:" + str(int(frame_no / caps_fps)) +
"[sec]")
logger.info('inference in %.4f seconds.' % time_estimation)
logger.debug('shape of image: ' + str(image.shape))
logger.debug(str(humans))
logger.info('shape of humans: ' + str(humans.shape))
if frame_no % int(caps_fps) == 0:
# for resetting number of graph.
hum_count = len(ma.humans_id)
# PLOT Pictures for movie
img = datum.cvOutputData
# plot cog & foot lines
if len(humans.shape) != 0:
cog_size = (calc_torso_length(humans) / 8).astype(int)
# if not skip_cog:
# for i in range(len(bodies_cog)):
# # plot center of gravity
# cv2.circle(img, (bodies_cog[i, 14, 0], bodies_cog[i, 14, 1]), cog_size[i, i], color=(0, 0, 0), thickness=-1)
# # plot foot line
# if bodies_cog[i, 6, 0]:
# dotline(img, (bodies_cog[i, 6, 0], 0), (bodies_cog[i, 6, 0], h_pxl), color=(10, 10, 10), thickness=2)
# if bodies_cog[i, 7, 0]:
# dotline(img, (bodies_cog[i, 7, 0], 0), (bodies_cog[i, 7, 0], h_pxl), color=(10, 10, 10), thickness=2)
for i, hum in enumerate(np.sort(ma.humans_id)):
hum_track = ma.humans_tracklet[ma.humans_tracklet[:, 1] == hum]
hum_track = hum_track.astype(int)
# plot cog & foot lines
if not skip_cog:
cv2.circle(img, (bodies_cog[i, 14, 0], bodies_cog[i, 14, 1]),
cog_size[i, i],
color=(0, 0, 0),
thickness=-1)
# plot foot line
if bodies_cog[i, 6, 0]:
dotline(img, (bodies_cog[i, 6, 0], 0),
(bodies_cog[i, 6, 0], h_pxl),
color=(10, 10, 10),
thickness=2)
if bodies_cog[i, 7, 0]:
dotline(img, (bodies_cog[i, 7, 0], 0),
(bodies_cog[i, 7, 0], h_pxl),
color=(10, 10, 10),
thickness=2)
# trajectories of COG
trajectories = np.array([(pos[39 * 3 + 2], pos[39 * 3 + 3])
for pos in hum_track
if pos[4 * 3 + 2] > 1])
cv2.polylines(img, [trajectories], False, (0, 0, 0), 3,
cv2.LINE_4)
# plot hands trajectories
if not skip_track:
trajectories = np.array([(pos[4 * 3 + 2], pos[4 * 3 + 3])
for pos in hum_track
if pos[4 * 3 + 2] > 1])
cv2.polylines(img, [trajectories], False,
(200, 200, int(hum % 3) * 30), 3, cv2.LINE_4)
# polydotline(img, [trajectories], (200, 200, int(i%3)*30))
trajectories = np.array([(pos[7 * 3 + 2], pos[7 * 3 + 3])
for pos in hum_track
if pos[7 * 3 + 2] > 1])
cv2.polylines(img, [trajectories], False,
(int(hum % 3) * 30, 200, int(hum % 3) * 30), 3,
cv2.LINE_4)
# polydotline(img, [trajectories], (200, 200, int(i%3)*30))
# plt graph of cog rate
if not plt_graph:
# save figure
cv2.imwrite(
os.path.join(
path_png_estimated,
video.split('.')[-2] + '{:06d}'.format(frame_no) + ".png"),
img)
else:
graph_row = 6 # if hum_count > 6 else 4
graph_col = graph_row + 2 + int(
(hum_count - graph_row if hum_count - graph_row > 0 else 0) /
graph_row)
fig = plt.figure(figsize=(16, 8))
grid_size = (graph_row, graph_col)
ax_img = plt.subplot2grid(grid_size, (0, 0),
rowspan=graph_row,
colspan=graph_row)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ax_img.imshow(img)
if len(ma.humans_id):
hum_idx = ~np.isnan(ma.humans_current[:, 19 * 3 + 2])
ax_img.set_xticks(ma.humans_current[hum_idx, 19 * 3 + 2])
ax_img.set_xticklabels(
list((ma.humans_id[hum_idx]).astype(str)))
# hum_c = ma.humans_current
for i, hum in enumerate(np.sort(ma.humans_id)):
if i == (graph_row * (graph_col - graph_row)):
break # count of humans is over the capacity
hum_track = ma.humans_tracklet[ma.humans_tracklet[:, 1] == hum]
hum_track = hum_track.astype(int)
ax_graph = plt.subplot2grid(
grid_size, (i - graph_row * int(i / graph_row),
graph_row + int(i / graph_row)))
hum_track = hum_track[~np.isnan(hum_track[:, 19 * 3 + 2])]
hum_track = hum_track[~np.isnan(hum_track[:, 22 * 3 + 2])]
if hum_track.shape[0] > 0:
foot = (hum_track[:, 39 * 3 + 2] - hum_track[:, 19 * 3 + 2]
) / (hum_track[:, 22 * 3 + 2] -
hum_track[:, 19 * 3 + 2])
line1 = ax_graph.plot(hum_track[:, 0], foot)
ax_graph.set_ylim([-0.2, 1.2])
ax_graph.set_xlim([hum_track[0, 0], hum_track[0, 0] + 30])
ax_graph.legend([str(hum)])
plt.savefig(
os.path.join(
path_png_estimated,
video.split('.')[-2] + '{:06d}'.format(frame_no) + ".png"))
plt.close()
plt.clf()
# before increment, renew some args
frame_no += 1
gc.collect()
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
logger.info("finish estimation & start encoding")
cmd = [
"ffmpeg", "-r",
str(caps_fps), "-start_number",
str(start_frame), "-i",
os.path.join(path_png_estimated,
video.split('.')[-2] + "%06d.png"), "-vcodec", "libx264",
"-pix_fmt", "yuv420p",
os.path.join(path_movie_out,
video.split('.')[-2] + ".mp4")
]
subprocess.call(cmd)
logger.debug('finished+')
def main():
#reset the joint.npy file at the beginning of time
reset_realtime_skeleton()
#saving the models
p = Path('/Users/kefei/Documents/real_time_skeletons/joints.npy')
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Flags
parser = argparse.ArgumentParser()
parser.add_argument(
"--image_path",
default="../../../examples/media/COCO_val2014_000000000192.jpg",
help=
"Process an image. Read all standard formats (jpg, png, bmp, etc.).")
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1: next_item = args[1][i + 1]
else: next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
try:
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
#Capture video from webcam
vid_capture = cv2.VideoCapture(0)
vid_cod = cv2.VideoWriter_fourcc(*'XVID')
joints = []
#output = cv2.VideoWriter("videos/cam_video.mp4", vid_cod, 20.0, (640,480))
t = 0
s = 0
while (True):
# Capture each frame of webcam video
ret, frame = vid_capture.read()
# Process Image
datum = op.Datum()
#imageToProcess = cv2.imread(args[0].image_path)
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
cv2.imshow("My cam video", datum.cvOutputData) # it was: frame
keypoints = datum.poseKeypoints #(1, 25, 3)
#only save coordinates of one person. only save when detected
#Maybe set an upper limit, like 200 frames in storage?
print(keypoints.shape)
if t % 2 == 0:
if keypoints.shape == (1, 25, 3):
with p.open('ab') as f:
s += 1
joints.append(keypoints)
np.save(f, keypoints)
if t > 200:
startTime = datetime.now()
joints.pop(
0
) #remove the first observation. then save the entire 200-frame
np.save(
'/Users/kefei/Documents/real_time_skeletons/joints.npy',
np.asarray(joints))
print(datetime.now() - startTime)
elif keypoints.shape[0] > 1:
if keypoints[0, :, :].shape == (25, 3):
first_person_points = keypoints[None, :, :]
print(first_person_joints.shape)
with p.open('ab') as f:
s += 1
joints.append(first_person_points)
np.save(f, first_person_points
) #save the first person it sees.
if t > 200:
joints.pop(0)
np.save(
'/Users/kefei/Documents/real_time_skeletons/joints.npy',
np.asarray(joints))
t += 1
#output.write(frame)
# Close and break the loop after pressing "x" key
k = cv2.waitKey(1)
if t > 200:
print('exceeds 200 frames')
if k == ord('x'):
break
elif k == ord('q'):
print('t=', t)
break
# close the already opened camera
print('t=', t)
print('s=', s)
#np.save('/Users/kefei/Documents/joints.npy',joints)
vid_capture.release()
# close the already opened file
#output.release()
# close the window and de-allocate any associated memory usage
cv2.destroyAllWindows()
# Display Image
#print("Body keypoints: \n" + str(datum.poseKeypoints))
#cv2.imshow("OpenPose 1.5.0 - Tutorial Python API", datum.cvOutputData)
#cv2.waitKey(0)
except Exception as e:
# print(e)
sys.exit(-1)
def openpose_demo(canvas):
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
params["hand"] = True
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
kinect_ir = PyKinectRuntime.PyKinectRuntime(
PyKinectV2.FrameSourceTypes_Infrared
| PyKinectV2.FrameSourceTypes_Depth)
kinect_color = PyKinectRuntime.PyKinectRuntime(
PyKinectV2.FrameSourceTypes_Color | PyKinectV2.FrameSourceTypes_Body
| PyKinectV2.FrameSourceTypes_Depth)
depth_width, depth_height = kinect_ir.depth_frame_desc.Width, kinect_ir.depth_frame_desc.Height
before_form = []
current_form = []
counter = 0
global rootFlag
while (True):
frame_ir = kinect_ir.get_last_infrared_frame()
frame_ir = np.uint8(frame_ir.clip(1, 4080) / 16.)
frame_ir = np.reshape(frame_ir,
(depth_height, depth_width)).astype(np.uint8)
ret, thresh1 = cv2.threshold(frame_ir, 130, 255, cv2.THRESH_BINARY)
thresh1 = correction(thresh1)
thresh1 = cv2.cvtColor(thresh1, cv2.COLOR_GRAY2RGB)
M = np.float32([[1, 0, 7], [0, 1, 0]])
kernel = np.ones((5, 5), np.uint8)
thresh1 = cv2.dilate(thresh1, kernel, iterations=1)
thresh1 = cv2.warpAffine(thresh1, M, (512, 424))
frame_color = kinect_color.get_last_color_frame()
frame_color = frame_color.reshape((1080, 1920, -1)).astype(np.uint8)
scale = max(512 / frame_color.shape[1], 424 / frame_color.shape[0])
frame_color = cv2.resize(frame_color, dsize=None, fx=scale, fy=scale)
frame_color = frame_color[:, 121:633]
frame_color = cv2.cvtColor(frame_color, cv2.COLOR_RGBA2RGB)
frame = cv2.bitwise_and(frame_color, thresh1)
frame_ir = None
frame_color = None
# Process Image
datum = op.Datum()
imageToProcess = frame
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
cv2.namedWindow("OpenPose 1.5.0 - Tutorial Python API",
cv2.WINDOW_KEEPRATIO
| cv2.WINDOW_NORMAL) #ウィンドウサイズを可変に
cv2.imshow("OpenPose 1.5.0 - Tutorial Python API", datum.cvOutputData)
#print(datum.handKeypoints[1][0])
try:
right_hand, flag = hm.is_hand_recog(datum.handKeypoints[1][0])
except Exception as e:
continue
#カメラの指定によっては「IndexError: too many indices for array」というエラーが出る
if flag == True:
current_form = hm.check_handform2(right_hand)
print(hm.list_to_num(current_form))
#print(current_form,counter)
if current_form == before_form: #1フレーム前の形と現在の形を比較する
counter = counter + 1 #同じだったらカウントを１増やす
if (counter == 2): #カウントが10になったら（10回連続して同じ形を認識したら）
canvas.delete("all")
n = hm.list_to_num(current_form) #手の形から番号を決定
try:
canvas.create_text(300, 300, text=n, font=('', 350))
except Exception as e:
break
else:
counter = 0 #違ったらカウントをリセットする
before_form = current_form
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if rootFlag == False:
break
cap.release()
cv2.destroyAllWindows()
def __init__(self, mode, with_sound):
self.cam = cv2.VideoCapture(0) # modify here for camera number
self.datum_hand = op.Datum()
self.hand_opWrapper = self.get_opWrapper_hand()
self.mode = mode
self.with_sound = with_sound
def run(self, frame):
height = []
width = []
center = []
# Process Image
datum = op.Datum()
datum.cvInputData = frame
self.opWrapper.emplaceAndPop([datum])
img_rd = datum.cvOutputData
# fall judge
try:
# key points have been identified
x = datum.poseKeypoints[0][:, 0]
y = datum.poseKeypoints[0][:, 1]
width.append(np.max(x[np.nonzero(x)]) - np.min(x[np.nonzero(x)]))
height.append(np.max(y[np.nonzero(y)]) - np.min(y[np.nonzero(y)]))
center.append(np.mean(x[np.nonzero(x)]))
center.append(np.mean(y[np.nonzero(y)]))
if self.frame_start_time == 0:
self.center0 = center.copy()
self.width0 = width.copy()
self.height0 = height.copy()
self.frame_start_time = time.time()
else:
diff = np.array(
[center[0] - self.center0[0], center[1] - self.center0[1]])
dist = math.sqrt(np.sum((diff * 10**(-4))**2))
now = time.time()
v = dist / (now - self.frame_start_time)
a = (v**2 - self.v0**2) / (2 * dist)
# print(v, abs(a))
if (abs(a) > 0.2) and \
(np.subtract(np.array(width), np.array(height)) > np.subtract(np.array(self.width0), np.array(
self.height0)) and np.subtract(np.array(width), np.array(height)) > 0):
self.couter += 1
# print("alarm by v and a")
elif (width > height and (x[8] != 0 or x[9] != 0 or x[12] != 0)
and v < 1):
self.couter += 1
# print("alarm by w and h")
else:
if self.error == 0:
self.error += 1
else:
self.couter = 0
self.error = 0
if self.couter > 3:
font = ImageFont.truetype("simsun.ttc", 30, index=1)
img_rd = Image.fromarray(
cv2.cvtColor(datum.cvOutputData, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(img_rd)
draw.text((10, 10),
text="Fall Detected",
font=font,
fill=(255, 0, 0))
img_rd = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR)
cv2.imwrite('fall_detection.jpg', frame)
t = threading.Thread(
target=post(event=3, imagePath='fall_detection.jpg'))
t.setDaemon(False)
t.start()
# status = post(event=3, imagePath='fall_detection.jpg')
# print("fall")
# update variables
self.frame_start_time = now
self.v0 = v
self.width0 = width.copy()
self.height0 = height.copy()
# if width > height:
# print("alarm")
self.firstFrame = None
except Exception as e:
gray = frame_process.preprocess_frame(frame)
if self.firstFrame is None:
self.firstFrame = gray
pass
frameDelta = cv2.absdiff(self.firstFrame, gray)
cnts = frame_process.get_contours(self.firstFrame, gray)
defined_min_area = 3000
frame, self.alert = algorithm_fall.fall_detect(
cnts, defined_min_area, frame, self.prevX, self.prevY,
self.xList, self.yList, self.centerV, self.alert)
img_rd = frame
# cv2.imshow("OpenPose 1.6.0 - Tutorial Python API", frame)
frame = cv2.resize(img_rd, (640, 480))
# cv2.imshow("OpenPose 1.6.0 - Tutorial Python API", img_rd)
return frame
# http://zhuooyu.cn:8000/api/person/old/10
def video_start():
global SSF_Wait, SLL_Wait, Pose, n, db, canvas, video_flag, cap, sx, sy, cursor, fill_line, canvas3, V
'''配置模型'''
params = dict() # 创建一个字典
params["model_folder"] = model # 修改路径
params["model_pose"] = "BODY_25" # 选择pose模型
params["number_people_max"] = 2 # 最大检测人体数
params["display"] = 2 # 2D模式
opWrapper = op.WrapperPython()
opWrapper.configure(params) # 导入上述参数
opWrapper.start()
'''配置模型'''
cap = cv2.VideoCapture(0) # 打开摄像头
Initial = 0
video_flag = True
week = str(strftime("%A", localtime()))
while video_flag: # 判断是否开启camera
SSF_Wait = 5
SLL_Wait = 2 # 加快video速度
cap.open(0)
ret, Frame = cap.read()
H = round(0.63 * sy)
W = round(0.906 * sy)
Frame = cv2.resize(Frame, (W, H)) # 图像尺寸变换
imageToProcess = cv2.cvtColor(Frame, cv2.COLOR_RGB2BGR) # RGB转换为BGR
BGR = imageToProcess
'''计算关节点'''
datum = op.Datum()
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
# print("Body keypoints: \n" + str(datum.poseKeypoints))
Pose = datum.poseKeypoints # 关键点存在的地方,若无人像，输出None
# BGR = datum.cvOutputData # imageToProcess 已识别后的图像
'''计算关节点'''
'''初值'''
if Initial == 0: # 赋初值
'''SSF'''
n = 0 # 默认user=0
j = 0
j1 = 0
j2 = 0
j3 = 0 # 4种状态
SSF = 0 # 计数
SSF_Angle = 270 # 实际角度
SSF_Max = 270 # 抬手最大值
SSF_Time = 0 # 测量间隔
SSF_Num = 3 # 间隔数
SSF_Hold = 0 # hold时间
SSF_Flag = 0 # 计时阀门关闭
SSF_start = 0 # 开始时间
SSF_Deviation = 0 # 0.18s角度差值
SSF_Sum = 0 # 角度差值和
hip_angle = 90 # hip角度
helper = 'None'
string2 = 'None'
comps = 0 # 补偿角
SSF_Row = 1 # 第1行开始记录
'''SLL'''
i = 0
i1 = 0
i2 = 0
i3 = 0
SLL = 0
SLL_Angle = 120 # 默认120为开始lift leg
SLL_Max = 90
SLL_Time = 0 # 测量间隔
SLL_Num = 3 # 间隔数
SLL_Hold = 0 # hold时间
SLL_Flag = 0 # 计时阀门关闭
SLL_start = 0 # 开始时间
SLL_Deviation = 0 # 0.18s角度差值
SLL_Sum = 0 # 角度差值和
SLL_Row = 1 # 第1行开始记录
SLL_string2 = 'None'
# 实际角度
V = 1
Initial = 1 # 赋值完毕
'''初值'''
'''模型测量'''
if str(Pose) != 'None':
'''Helper'''
if Pose.shape[0] == 1:
n = 0 # Only one person
helper = 'Yes!'
else:
if Pose[1][8][2] != 0 and Pose[1][9][2] != 0 and Pose[1][12][2] != 0:
if Pose[0][8][2] != 0 and Pose[0][9][2] != 0 and Pose[0][12][2] != 0:
if Pose[0][8][1] > Pose[1][8][1] and Pose[0][9][1] > Pose[1][9][1] and Pose[0][12][1] > \
Pose[1][12][1]:
n = 0 # 0先生是坐着的
else:
n = 1
else:
n = 1
else:
n = 0
helper = 'No'
'''Helper''' # n = user
'''Compensation'''
# neck_angle = round(Angle(5, 1, 8), 0)
comps = np.arctan(abs((Pose[n][1][0] - Pose[n][8][0])) / (Pose[n][8][1] - Pose[n][1][1])) * 180 / np.pi
comps = round(comps, 0)
if comps < 10:
string2 = 'Straight!'
comps = 0
else:
if Pose[n][1][0] > Pose[n][8][0]:
string2 = 'Left Comps! ' + str(comps)
else:
string2 = 'Right Comps! ' + str(abs(comps))
if SSF_Max > Angle(1, 2, 3) and j == 1 and j1 == 1 and j2 == 1 and j3 == 1:
SSF_Max = round(Angle(1, 2, 3), 0) # 抬手时找角度最小
# real_angle > Angle(1, 2, 3) - (180 - hip_angle - neck_angle)
'''Compensation'''
'''SSF'''
if Pose[n][4][2] != 0 and Pose[n][2][2] != 0 and Pose[n][1][2] != 0 and Pose[n][3][2] != 0:
'''Num'''
SSF_Deviation = abs(SSF_Angle - Angle(1, 2, 3))
SSF_Angle = Angle(1, 2, 3)
if Pose[n][4][1] < Pose[n][2][1]: # hands up
j3 = 1
else: # hands down
j3 = 0
if j == 0 and j1 == 0 and j2 == 1 and j3 == 1: # 0011抬手
SSF = SSF + 1
j = j1
j1 = j2
j2 = j3
'''Num'''
'''Hold time'''
if j == 1 and j1 == 1 and j2 == 1 and j3 == 1: # 1111 hands up
SSF_Time = SSF_Time + 1 # 单位0.18s
if SSF_Time % SSF_Num == 0: # 开始判定
if SSF_Sum / SSF_Num < 6: # if hold？
if SSF_Flag == 0:
SSF_start = time()
SSF_Flag = 1
else:
SSF_Flag = 0
SSF_Sum = 0
else: # 角度均值
SSF_Sum = SSF_Deviation + SSF_Sum
else:
SSF_Flag = 0
if SSF_Flag == 1 and round(time() - SSF_start, 2) > 1: # 找到最后的hold值
SSF_Hold = round(time() - SSF_start, 2)
'''Hold time'''
'''Fill in database'''
if j == 1 and j1 == 1 and j2 == 0 and j3 == 0: # 1100放手
sql = "INSERT INTO " + week + "(SSF_Times, SSF_Angle, SSF_Hold, SSF_Comps ,SSF_DIY) VALUES (%s, %s, " \
"%s, %s, %s); "
cursor.execute(sql, (SSF_Row, 270 - SSF_Max, SSF_Hold, comps, helper))
db.commit()
SSF_Row = SSF_Row + 1
SSF_Max = 270
SSF_Hold = 0
'''Fill in database'''
else:
BGR = cv2.putText(BGR, 'Move left', (5, H - 90), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 2)
'''SSF'''
###################################################################################################
'''Sitting Leg Lift'''
if Pose[n][9][2] != 0 and Pose[n][10][2] != 0 and Pose[n][11][2] != 0:
'''COMPS'''
SLL_comps = np.arctan(
abs((Pose[n][9][1] - Pose[n][10][1]) / (Pose[n][10][0] - Pose[n][9][0]))) * 180 / np.pi
SLL_comps = round(SLL_comps, 0)
if SLL_comps < 15:
SLL_string2 = 'Horizontal'
SLL_comps = 0
else:
SLL_string2 = 'Comps! ' + str(abs(SLL_comps))
'''COMPS'''
'''Num'''
SLL_Deviation = SLL_Angle - Angle(9, 10, 11)
SLL_Angle = Angle(9, 10, 11)
if SLL_Max < SLL_Angle:
SLL_Max = round(SLL_Angle, 0)
if SLL_Angle > 120: # leg up
i3 = 1
else: # leg down
i3 = 0
if i == 0 and i1 == 0 and i2 == 1 and i3 == 1: # 0011抬脚
SLL = SLL + 1
i = i1
i1 = i2
i2 = i3
'''Num'''
'''Hold time'''
if i == 1 and i1 == 1 and i2 == 1 and i3 == 1: # 1111 hands up
SLL_Time = SLL_Time + 1 # 单位0.18s
if SLL_Time % SLL_Num == 0: # 开始判定
if SLL_Sum / SSF_Num < 6: # if hold？
if SLL_Flag == 0:
SLL_start = time()
SLL_Flag = 1
else:
SLL_Flag = 0
SLL_Sum = 0
else: # 角度偏差均值
SLL_Sum = SLL_Deviation + SLL_Sum
else:
SLL_Flag = 0
if SLL_Flag == 1 and round(time() - SLL_start, 2) > 1: # 找到最后的hold值
SLL_Hold = round(time() - SLL_start, 2)
'''Hold time'''
'''Fill in database'''
if i == 1 and i1 == 1 and i2 == 0 and i3 == 0: # 1100放腿
sql = "INSERT INTO " + week + "(SLL_Times, SLL_Angle, SLL_Hold, SLL_Comps ,SLL_DIY) VALUES (%s, %s, " \
"%s, %s, %s); "
cursor = db.cursor()
cursor.execute(sql, (SLL_Row, SLL_Max, SLL_Hold, SLL_comps, helper))
db.commit()
SLL_Row = SLL_Row + 1
SLL_Max = 90
SLL_Hold = 0
'''Fill in database'''
else:
BGR = cv2.putText(BGR, 'Move left', (5, H - 90), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 2)
'''SLL'''
else:
BGR = cv2.putText(BGR, 'Move into camera', (5, H - 60), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
'''mirror_feedback'''
string0 = 'SSF: ' + str(SSF) + ' Hold:' + str(SSF_Hold) + 's'
BGR = cv2.putText(BGR, string0, (5, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 2)
BGR = cv2.putText(BGR, 'Angle: ' + str(270 - SSF_Max), (5, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 2)
BGR = cv2.putText(BGR, string2, (5, 110), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 2)
BGR = cv2.putText(BGR, helper, (5, 150), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 2)
############################################################################################
SLL_string0 = 'SLL: ' + str(SLL) + ' Hold:' + str(SLL_Hold) + 's'
BGR = cv2.putText(BGR, SLL_string0, (W - 350, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
BGR = cv2.putText(BGR, 'Angle: ' + str(SLL_Max), (W - 350, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
BGR = cv2.putText(BGR, SLL_string2, (W - 350, 110), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
BGR = cv2.putText(BGR, helper, (W - 350, 150), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
'''控件显示'''
# mirror feedback
RGB = Image.fromarray(BGR)
RGB = ImageTk.PhotoImage(RGB)
canvas.create_image(0, 0, anchor='nw', image=RGB) # 刷新图片，nw表示从西北角开始排列图片
canvas.bg = RGB
text2.set('Camera Open!')
# times
print(SSF, V)
if int(SSF) < int(V):
text3.set(str(SSF) + ' / ' + str(V))
else:
text3.set('Finish!')
# progress bar
if j == 1 and j1 == 1 and j2 == 1 and j3 == 1: # hands up
k = 0.4 * sx / 90 # 总长度0.4*sx
n = k * (180 - SSF_Max)
else:
n = 0
canvas3.coords(fill_line, (0, 0, n, 60))
window.update()
cv2.waitKey(1)
def pullUpDetect(length):
# Process
cnt = 0
results = []
tendency = []
result = {}
r_elbow_angle_list = []
l_elbow_angle_list = []
eye_distance_list = []
tick = []
pullUpCnt = 0
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# get video from webcam or video
start = time.time()
cap = cv2.VideoCapture(0)
cap_t = cv2.VideoCapture(1)
# cap2 = cv2.VideoCapture(r'E:\University\科研创新\雏燕计划-体测\体测姿势素材\push-up\push-up-test-1.mp4')
coorList = []
start_time = datetime.now()
while True:
# Get images from cam
ret, imageToProcess = cap.read()
ret, imageToTest = cap_t.read()
cv2.imshow('video', imageToProcess)
cv2.imshow('2', imageToTest)
cur_time = datetime.now()
delta_time = (cur_time - start_time).seconds
print('\r倒计时：' + length - delta_time + '秒', end='')
if delta_time == length:
break
if cnt % 2 == 0:
datum = op.Datum()
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
if datum.poseKeypoints.size != 1:
coor = kpp.getKeyPoints(datum.poseKeypoints[0])
if coor:
r_elbow_angle = getValue.getElbowAngle(coor, 'R')
l_elbow_angle = getValue.getElbowAngle(coor, 'L')
eye_distance = getValue.getEyeWristDistance(coor)
if l_elbow_angle:
l_elbow_angle_list.append(l_elbow_angle)
if eye_distance:
eye_distance_list.append(eye_distance)
if r_elbow_angle:
r_elbow_angle_list.append(r_elbow_angle)
tick.append(r_elbow_angle)
if len(tick) == 5:
tend = analysis.getTendency(tick, 8) # One tick
tick = []
if tend:
tendency.append(tend)
if 3 <= len(tendency):
if tendency[-1] == 'down' or tendency[
-1] == 'stable':
if tendency[-2] == 'upper' and tendency[
-3] == 'upper': # a period
pullUpCnt += 1
result['Num'] = pullUpCnt
standard = analysis.pullUpPeriodJudge(
r_elbow_angle_list,
l_elbow_angle_list,
eye_distance_list)
result['IsRElbowStandard'], result[
'IsLElbowStandard'], result[
'IsHeightStandard'], total = standard
r_elbow_angle_list = l_elbow_angle_list = eye_distance_list = []
results.append(result)
print(result)
result = {}
db.ret(delta_time, total, pullUpCnt,
'pullUp', results, True)
cv2.imshow("OpenPose 1.5.1 - Tutorial Python API",
datum.cvOutputData)
if cv2.waitKey(1) == ord('q'):
break
cnt += 1
db.ret(None, None, None, None, None, False)
def openpose_demo(canvas):
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
params["hand"] = True
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
cap = cv2.VideoCapture(1) #0だったり1だったり
#cap.set(3,1280)
#cap.set(4,960)
cap.set(3, 1920)
cap.set(4, 1080)
before_form = []
current_form = []
counter = 0
global rootFlag
while (True):
ret, frame = cap.read()
#cv2.imshow('Raw Frame', frame)
#frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
#frame = cv2.cvtColor(frame,cv2.COLOR_GRAY2RGB)
#frame = adjust(frame,alpha=1.0,beta=-25.0)
# Process Image
datum = op.Datum()
imageToProcess = frame
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
cv2.namedWindow("OpenPose 1.5.0 - Tutorial Python API",
cv2.WINDOW_KEEPRATIO
| cv2.WINDOW_NORMAL) #ウィンドウサイズを可変に
cv2.imshow("OpenPose 1.5.0 - Tutorial Python API", datum.cvOutputData)
#print(datum.handKeypoints[1][0])
try:
right_hand, flag = hm.is_hand_recog(datum.handKeypoints[1][0])
except Exception as e:
continue
#カメラの指定によっては「IndexError: too many indices for array」というエラーが出る
if flag == True:
current_form = hm.check_handform2(right_hand)
#print(current_form)
if current_form == before_form: #1フレーム前の形と現在の形を比較する
counter = counter + 1 #同じだったらカウントを１増やす
if (counter == 5): #カウントが10になったら（10回連続して同じ形を認識したら）
canvas.delete("all")
n = hm.list_to_num(current_form) #手の形から番号を決定
try:
canvas.create_text(300, 300, text=n, font=('', 350))
except Exception as e:
break
else:
counter = 0 #違ったらカウントをリセットする
try:
canvas.create_text(300, 300, text=" ", font=('', 350))
except Exception as e:
break
before_form = current_form
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if rootFlag == False:
break
cap.release()
cv2.destroyAllWindows()
def openpose_demo(canvas):
upperbody_cascade = cv2.CascadeClassifier(upperbody_cascade_path)
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
params["hand"] = True
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
cap = cv2.VideoCapture(0) #0だったり1だったり
cap.set(3, 1280)
cap.set(4, 960)
before_form = []
current_form = []
counter = 0
global rootFlag
while (True):
ret, frame = cap.read()
frame = adjust(frame, alpha=1.0, beta=-25.0)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bodys = upperbody_cascade.detectMultiScale(gray)
max_x, max_y, max_h, max_w = max_body_select(bodys)
frame2 = frame[max_y:max_y + max_h, max_x:max_x + max_w]
# Process Image
datum = op.Datum()
imageToProcess = frame2
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
try:
if datum.poseKeypoints[0][0][2] < 0.6:
continue
#頭部の座標を取得
head_x = int(datum.poseKeypoints[0][0][0])
head_y = int(datum.poseKeypoints[0][0][1])
top = head_y - 240
bottom = head_y + 240
left = head_x - 320
right = head_x + 320
frame2 = frame[top:bottom, left:right]
#cv2.namedWindow("OpenPose 1.5.0 - Tutorial Python API", cv2.WINDOW_KEEPRATIO | cv2.WINDOW_NORMAL) #ウィンドウサイズを可変に
#cv2.imshow("OpenPose 1.5.0 - Tutorial Python API", datum.cvOutputData)
del datum
# Process Image
datum2 = op.Datum()
datum2.cvInputData = frame2
opWrapper.emplaceAndPop([datum2])
cv2.imshow("zoom image", datum2.cvOutputData)
try:
right_hand, flag = hm.is_hand_recog(datum2.handKeypoints[1][0])
except Exception as e:
continue
#カメラの指定によっては「IndexError: too many indices for array」というエラーが出る
if flag == True:
current_form = hm.check_handform2(right_hand)
#print(current_form,counter)
if current_form == before_form: #1フレーム前の形と現在の形を比較する
counter = counter + 1 #同じだったらカウントを１増やす
if (counter == 2): #カウントが10になったら（10回連続して同じ形を認識したら）
canvas.delete("all")
n = hm.list_to_num(current_form) #手の形から番号を決定
try:
canvas.create_text(300,
300,
text=n,
font=('', 350))
except Exception as e:
break
else:
counter = 0 #違ったらカウントをリセットする
before_form = current_form
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if rootFlag == False:
break
except Exception as e:
continue
cap.release()
cv2.destroyAllWindows()
def detect_keypoints(image):
# Process Image
datum = op.Datum()
datum.cvInputData = image
opWrapper.emplaceAndPop([datum])
return datum
def pose_estimation(self): # 调用openpose识别出关节点
# load openpose python api
# if self.arg.openpose is not None:
# sys.path.append('{}/python'.format(self.arg.openpose))
# sys.path.append('{}/build/python'.format(self.arg.openpose))
# sys.path.append('{}/build/python/openpose'.format(self.arg.openpose))
# sys.path.append('{}/build/example/openpose'.format(self.arg.openpose))
# sys.path.append('{}/build/python/openpose/Release'.format(self.arg.openpose))
# sys.path.append('{}/build/x64/Release'.format(self.arg.openpose))
# sys.path.append('{}/build/bin'.format(self.arg.openpose))
# try:
# #from openpose import pyopenpose as op
# import pyopenpose as op
# except:
# print('Can not find Openpose Python API.')
# return
# dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
# if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
# sys.path.append('D:/PycharmProject/openpose/build/python/openpose/Release')
# os.environ['PATH'] = os.environ['PATH'] + ';' + dir_path + 'D:/PycharmProject/openpose/build/x64/Release' + dir_path + 'D:/PycharmProject/openpose/build/bin'
sys.path.append(dir_path +
'/../../openpose/build/python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../openpose/build/x64/Release;' + dir_path + '/../../openpose/build/bin;'
import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
video_name = self.arg.video.split('/')[-1].split('.')[0]
# initiate
opWrapper = op.WrapperPython()
params = dict(model_folder='./models', model_pose='COCO')
opWrapper.configure(params)
opWrapper.start()
self.model.eval()
video_capture = cv2.VideoCapture(self.arg.video)
# video_capture = cv2.VideoCapture(0) ＃ ０为打开摄像头
video_length = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
pose_tracker = naive_pose_tracker(data_frame=video_length)
# pose estimation
start_time = time.time()
frame_index = 0
video = list()
while (True):
# get image
ret, orig_image = video_capture.read()
if orig_image is None:
break
source_H, source_W, _ = orig_image.shape
orig_image = cv2.resize(orig_image,
(256 * source_W // source_H, 256))
H, W, _ = orig_image.shape
video.append(orig_image)
# pose estimation
datum = op.Datum()
datum.cvInputData = orig_image
# opWrapper.emplaceAndPop([datum])
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
multi_pose = datum.poseKeypoints # (num_person, num_joint, 3)
#if len(multi_pose.shape) != 3: # 是否三维数组
if (multi_pose is None): # 是否有数据，否则为None的时候会报错
continue
# normalization
multi_pose[:, :, 0] = multi_pose[:, :, 0] / W
multi_pose[:, :, 1] = multi_pose[:, :, 1] / H
multi_pose[:, :, 0:2] = multi_pose[:, :, 0:2] - 0.5
multi_pose[:, :, 0][multi_pose[:, :, 2] == 0] = 0
multi_pose[:, :, 1][multi_pose[:, :, 2] == 0] = 0
# pose tracking
pose_tracker.update(multi_pose, frame_index)
frame_index += 1
print('Pose estimation ({}/{}).'.format(frame_index, video_length))
data_numpy = pose_tracker.get_skeleton_sequence()
return video, data_numpy
def detect_skeleton(file_name):
try:
# Import Openpose (Windows/Ubuntu/OSX)
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../openpose/build/python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + '../../openpose/build/x64/Release;' + '../../openpose/build/bin'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../openpose/build/python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Flags
parser = argparse.ArgumentParser()
parser.add_argument("--video_path",
default=f"../media/{file_name}.mp4",
help="Read input video (avi, mp4).")
parser.add_argument("--no_display",
default=False,
help="Enable to disable the visual display.")
args = parser.parse_known_args()
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../openpose/models/"
params["disable_multi_thread"] = "false"
numberGPUs = 1
# Add others in path?
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1:
next_item = args[1][i + 1]
else:
next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
# Construct it from system arguments
# op.init_argv(args[1])
# oppython = op.OpenposePython()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Video
datum = op.Datum()
cap = cv2.VideoCapture(args[0].video_path)
set_frame_size(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
# 관절을 입힌 동영상 생성
# fourcc = cv2.VideoWriter_fourcc('D', 'I', 'V', 'X')
# out = cv2.VideoWriter(f'../output/output/video/{file_name}.avi', fourcc, 20.0, get_frame_size())
frame_data = []
frame_id = -1
while cap.isOpened():
frame_id += 1
grabbed, frame = cap.read()
if frame is None or not grabbed:
print("Finish reading video frames...")
break
datum.cvInputData = frame
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
# print("Body keypoints: \n" + str(datum.poseKeypoints))
# 동영상 저장
#out.write(datum.cvOutputData)
# cv2.imshow("OpenPose 1.7.0 - Tutorial Python API", datum.cvOutputData)
# cv2.waitKey(1)
# save the keypoint as a list
frame_data.append(make_json(datum, frame_id))
else:
print('cannot open the file')
# show list as json
with open(f'../output/json/output{file_name}.json',
'w',
encoding="utf-8") as make_file:
json.dump(frame_data, make_file, ensure_ascii=False, indent="\t")
cap.release()
# out.release()
# cv2.destroyAllWindows()
except Exception as e:
print(e)
sys.exit(-1)
return frame_data
def main():
try:
'''
Part to get openpose & pyopenpose from built openpose.
Be sure to build the openpose system first.
Feel free to change directories of built python packages.
'''
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/../../python/openpose/Release')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
import pyopenpose as op
# from openpose import *
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
# from openpose import *
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
'''
Customizing part for Openpose Python Wrapper.
For the project, only enable body & hand.
'''
params = dict()
params["model_folder"] = "../../../models/"
params["hand"] = True
params["hand_detector"] = 2
params["body"] = 1
params["net_resolution"] = '320x192' #20*11
# params["face"] = True
# params["disable_blending"] = True
# params["fps_max"] = 5
handRectangles = [[
op.Rectangle(128, 0, 1024, 1024),
op.Rectangle(0., 0., 0., 0.)
]]
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
datum.handRectangles = handRectangles
cam = cv2.VideoCapture(0) # modify here for camera number
cam.set(3, 1280)
cam.set(4, 1024)
pair_poseKeypoints = [[], []]
input_hands = []
prev_state = None
'''
Loading Model for Recognizing Hand Gestures
Please refer to model.py for various models trained for the task.
Current implementaiton is based on cnn_resample_100.pt which shows best demo performance.
If you would like to use other models, be sure to modify the codes below properly.
'''
model = HandGestureNet(n_channels=42, n_classes=5)
model.load_state_dict(torch.load('cnn_resample_100.pt'))
# model.load_state_dict(torch.load('CNN_mid12.pt'))
model.to('cuda')
model.eval()
'''
Message state contains time of state changes.
Used to prevent burst of messages in short time to the Zoom chat.
'''
msg_state = ('not_sent', time.perf_counter())
'''
Starting the Project
Pose & Hand Gesture Estimation
'''
while (cv2.waitKey(1) != 27):
if msg_state[0] == 'sent':
if time.perf_counter() - msg_state[1] > 2.5:
msg_state = ('not_sent', time.perf_counter())
ret, frame = cam.read()
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
frame = datum.cvOutputData
'''If Person not in Camera'''
if datum.poseKeypoints.shape == ():
if msg_state[0] == 'not_sent':
# print('WHY NOT WORKING')
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_absent, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 3
fontColor = (0, 0, 255)
lineType = 2
fontThickness = 2
msg_on_screen = 'ABSENT!'
textsize = cv2.getTextSize(msg_on_screen, font, fontScale,
fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
cv2.rectangle(frame, (0, 0), (1280, 1024), (0, 0, 255), 20)
cv2.putText(frame, 'Absent!', bottomLeftCornerOfText, font,
fontScale, fontColor, lineType)
cv2.imshow("Openpose 1.4.0 Webcam", frame)
continue
'''If there are more than one people on camera'''
if len(datum.poseKeypoints) > 1:
if prev_state is not None and prev_state[0] == 'multi_people':
if prev_state[1] > 2:
if msg_state[0] == 'not_sent':
# print('WHY NOT WORKING')
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_multi, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 3
fontColor = (0, 0, 255)
lineType = 2
fontThickness = 2
msg_on_screen = 'MUTLIPLE PEOPLE!'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale, fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
cv2.rectangle(frame, (0, 0), (1280, 1024), (0, 0, 255),
20)
cv2.putText(frame, msg_on_screen,
bottomLeftCornerOfText, font, fontScale,
fontColor, lineType)
cv2.imshow("Openpose 1.4.0 Webcam", frame)
msg_state = ('sent', time.perf_counter())
continue
else:
prev_state = ('multi_people', time.perf_counter())
'''Evaluate Movement & Confidence'''
del pair_poseKeypoints[0]
pair_poseKeypoints.append(datum.poseKeypoints[0])
body_confidence_avg = avg_pose_confidence(datum.poseKeypoints[0])
# print(body_confidence_avg)
moved = metric(pair_poseKeypoints)
'''
Evaluate Hand Gesture
Please modify the below if you use other models for hand gestures.
Refer to model.py
'''
# if len(input_hands) == 12: # normalizev2.pt
# del input_hands[0]
# input_hands.append(datum.handKeypoints[0][0])
# # print(len(input_hands))
# prob, gesture = None, None
# hand_confidence_avg = avg_list_confidence(input_hands)
# # if len(input_hands) == 12 and avg >= 0.1:
# if len(input_hands) == 12:
# # print('Confidence : ', hand_confidence_avg)
# prob, gesture = get_hand_gesture(model, input_hands)
# # print(prob, gesture)
norm_hand = normalize(datum.handKeypoints[0][0])
# print(norm_hand.shape)
if len(input_hands) == 100:
for i in range(5):
del input_hands[0]
for i in range(5):
input_hands.append(norm_hand)
# print(len(input_hands))
prob, gesture = None, None
hand_confidence_avg = avg_list_confidence(input_hands)
# if len(input_hands) == 12 and avg >= 0.1:
if len(input_hands) == 100:
# print('Confidence : ', hand_confidence_avg)
# print(input_hands[0][:][:2])
inputs = torch.FloatTensor(input_hands).to('cuda')
inputs = inputs[:, :, :2]
# print(inputs.size())
# prob, gesture = get_hand_gesture_cnn(model, input_hands[:][:][:1])
prob, gesture = get_hand_gesture_cnn(model, inputs)
print(prob, gesture)
'''Output Recognition Results'''
print_msg = False
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = None
fontColor = None
fontScale = 3
fontThickness = 2
msg_on_screen = None
'''Thumbs DOWN'''
if valid_hand(hand_confidence_avg, gesture) and gesture == 1:
print('THUMBS DOWN PROB : ', prob)
if prob > 0: # mitigated by using softmax
'''Counter'''
if prev_state is None:
prev_state = ('thumbs_down', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 5.5:
prev_state = ('thumbs_down', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'thumbs_down':
prev_state = ('thumbs_down', time.perf_counter())
# print(prev_state)
else:
# print(time.perf_counter() - prev_state[1])
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_thumbsdown, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
if time.perf_counter() - prev_state[1] > 0.5:
print_msg = True
# bottomLeftCornerOfText = (450, 500)
fontColor = (255, 0, 0)
fontScale = 3
msg_on_screen = 'THUMBS DOWN'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale,
fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) //
2,
(1024 + textsize[1]) //
2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
'''Thumbs UP'''
elif valid_hand(hand_confidence_avg, gesture) and gesture == 2:
print('THUMBS UP PROB : ', prob)
'''Counter'''
if prob > 0:
if prev_state is None:
prev_state = ('thumbs up', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 5.5:
prev_state = ('thumbs_up', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'thumbs_up':
prev_state = ('thumbs_up', time.perf_counter())
# print(prev_state)
else:
# print(time.perf_counter() - prev_state[1])
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_thumbsup, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
if time.perf_counter() - prev_state[1] > 0.5:
print_msg = True
# bottomLeftCornerOfText = (450, 500)
fontColor = (0, 255, 0)
fontScale = 3
msg_on_screen = 'THUMBS UP'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale,
fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) //
2,
(1024 + textsize[1]) //
2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
'''RAISED HAND'''
elif valid_hand(hand_confidence_avg, gesture) and gesture == 4:
print('RAISE HAND PROB : ', prob)
'''Counter'''
if prev_state is None:
prev_state = ('raise_hand', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 5.5:
prev_state = ('raise_hand', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'raise_hand':
prev_state = ('raise_hand', time.perf_counter())
# print(prev_state)
else:
# print(time.perf_counter() - prev_state[1])
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_raisehand, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
if time.perf_counter() - prev_state[1] > 0.5:
print_msg = True
bottomLeftCornerOfText = (450, 500)
fontColor = (0, 255, 255)
fontScale = 3
msg_on_screen = 'HAND RAISED'
textsize = cv2.getTextSize(msg_on_screen, font,
fontScale, fontThickness)[0]
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
'''
Movement Detected
Please refer to metric.py for detection metrics
'''
elif moved:
'''Counter'''
if prev_state is None:
prev_state = ('detect_move', time.perf_counter())
# print(prev_state)
elif prev_state[0] == 'rest':
if time.perf_counter() - prev_state[1] > 1.5:
prev_state = ('detect_move', time.perf_counter())
# print(prev_state)
elif prev_state[0] != 'detect_move':
prev_state = ('detect_move', time.perf_counter())
# print(prev_state)
else:
# print(msg_state)
if msg_state[0] == 'not_sent':
conn.request(
"POST",
"/v2/chat/users/[email protected]/messages",
payload_movement, headers)
#
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
msg_state = ('sent', time.perf_counter())
print_msg = True
bottomLeftCornerOfText = (150, 500)
fontColor = (0, 0, 255)
fontScale = 3
msg_on_screen = 'MOVEMENT DETECTED'
textsize = cv2.getTextSize(msg_on_screen, font, fontScale,
fontThickness)[0]
# print(textsize)
bottomLeftCornerOfText = ((1280 - textsize[0]) // 2,
(1024 + textsize[1]) // 2)
# if time.perf_counter() - prev_state[1] > 3.5:
# prev_state = ('rest', time.perf_counter())
'''Print messages according to states above'''
if print_msg:
lineType = 2
cv2.rectangle(frame, (0, 0), (1280, 1024), fontColor, 40)
cv2.putText(frame, msg_on_screen, bottomLeftCornerOfText, font,
fontScale, fontColor, lineType)
cv2.imshow("Openpose 1.4.0 Webcam", frame) # datum.cvOutputData
# Always clean up
cam.release()
cv2.destroyAllWindows()
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
print(e)
sys.exit(-1)
def main():
params = set_params()
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
webcam = VideoStream(src=0).start()
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
frame = webcam.read()
frame = cv2.resize(frame, (400, 300))
datum = op.Datum()
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
# cam = datum.cvOutputData
cam = frame
# print(type(datum.poseKeypoints))
# print(isinstance(datum.poseKeypoints, np.ndarray))
# print(datum.poseKeypoints.size)
# if (type(datum.poseKeypoints) == )
# nose position
if (isinstance(datum.poseKeypoints, np.ndarray)
and datum.poseKeypoints.size == 75):
noseX = datum.poseKeypoints[0][0][0]
noseY = datum.poseKeypoints[0][0][1]
cv2.circle(cam, (noseX, noseY), 5, (0, 255, 255))
neckX = datum.poseKeypoints[0][1][0]
neckY = datum.poseKeypoints[0][1][1]
cv2.circle(cam, (neckX, neckY), 5, (0, 255, 255))
rWristX = datum.poseKeypoints[0][4][0]
rWristY = datum.poseKeypoints[0][4][1]
cv2.circle(cam, (rWristX, rWristY), 5, (0, 255, 255))
right_hand_up(cam, (noseX, noseY), (neckX, neckY),
(rWristX, rWristY))
lWristX = datum.poseKeypoints[0][7][0]
lWristY = datum.poseKeypoints[0][7][1]
cv2.circle(cam, (lWristX, lWristY), 5, (0, 255, 255))
left_hand_up(cam, (noseX, noseY), (neckX, neckY),
(lWristX, lWristY))
# noseX = datum.poseKeypoints[0][0][0]
# noseY = datum.poseKeypoints[0][0][1]
# cv2.circle(cam, (noseX, noseY), 5, (0, 255, 255))
# kPoints = datum.poseKeypoints[0, i, :, :]
# Display the stream
# cv2.resize(cam, (256, 144))
cv2.imshow('Est', cam)
# keyboard interactions
key = cv2.waitKey(1)
if key == ord('q'):
break
cv2.destroyAllWindows()
def test_SVM_Classifier(test_images_path, isPCAon=True):
#
# try to predict some input images with/without yoga poses
#
if test_images_path == "":
return
#region init OpenPose
# Import Openpose (Windows/Ubuntu/OSX)
dir_path = os.path.dirname(os.path.abspath(__file__))
try:
# Windows Import
if platform == "win32":
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append(dir_path + '/dependencies/openpose/libs')
os.environ['PATH'] = os.environ[
'PATH'] + ';' + dir_path + '/dependencies/openpose/dlls;'
import pyopenpose as op
else:
# Change these variables to point to the correct folder (Release/x64 etc.)
sys.path.append('../../python')
# If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
# sys.path.append('/usr/local/python')
from openpose import pyopenpose as op
except ImportError as e:
print(
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "./dependencies/openpose/models/"
params["net_resolution"] = "320x176"
# Flags
parser = argparse.ArgumentParser()
args = parser.parse_known_args()
# parse other params passed
for i in range(0, len(args[1])):
curr_item = args[1][i]
if i != len(args[1]) - 1:
next_item = args[1][i + 1]
else:
next_item = "1"
if "--" in curr_item and "--" in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = "1"
elif "--" in curr_item and "--" not in next_item:
key = curr_item.replace('-', '')
if key not in params: params[key] = next_item
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# endregion
# test images path
test_images_path = 'yoga-pose-images-for-test'
# get all images
images = get_all_images_recursively(test_images_path)
# hold combined features
record_details = []
# hold image indexes of appropriate combined features
img_idxs_of_records = []
# hold test images data
images_data = []
for idx in range(0, len(images)):
print("\nCurrent image: " + images[idx] + "\n")
# get image full name (with file extension)
_, image_full_name = os.path.split(images[idx])
# image name without extension
image_name = image_full_name[0:image_full_name.rindex('.')]
# processed by OpenPose
datum = op.Datum()
imageToProcess = cv2.imread(images[idx])
img_rgb = cv2.cvtColor(imageToProcess, cv2.COLOR_BGR2RGB)
# add image data to list
images_data.append(img_rgb)
height, width, channels = imageToProcess.shape
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
# print("Body keypoints: \n" + str(datum.poseKeypoints))
# check if exists pose key points data
if not (datum.poseKeypoints.size < 2):
# merging bounding boxes if applicable
merged_poseKeypoints = bounding_box_merging(
datum.poseKeypoints,
ratio_of_intersec_thresh=0.36,
ratio_of_distance_thresh=2)
# assign merged_poseKeypoints to datum.poseKeypoints array
datum.poseKeypoints = merged_poseKeypoints
# draw bounding box on test image
drawn_img = draw_bounding_box(datum)
drawn_img = cv2.cvtColor(drawn_img, cv2.COLOR_BGR2RGB)
images_data[idx] = drawn_img
# an array to save mixed features (keypoints and HOG) of an image
keyPoint_HOG_feats_arrs = []
if datum.poseKeypoints.size > 1 and datum.poseKeypoints.ndim == 3:
# region extract keypoints features
keypoints_coordinates = datum.poseKeypoints[:, :, :2]
# translate and scale keypoints by its center and box size
flatted_features = normalize_keypoint_by_its_bounding_box(
keypoints_coordinates)
# image_size_in_str = str(width) + 'x' + str(height)
for row in flatted_features:
keyPoint_HOG_feats_arrs.append(row)
# endregion
# region extract HOG features
extracted_hog_feats_arrs = \
extract_ROI_and_HOG_feature(datum,
image_name,
isDisplayImageWithROIs=False,
isExtractRoiHogBitmap=False)
for i in range(0, len(extracted_hog_feats_arrs)):
keyPoint_HOG_feats_arrs[i] = np.append(
keyPoint_HOG_feats_arrs[i],
extracted_hog_feats_arrs[i])
# endregion
# add features data to accumulate array
for row in keyPoint_HOG_feats_arrs:
record_details.append(row)
img_idxs_of_records.append(idx)
if len(record_details) > 0:
# normalize feature vector
X = pd.DataFrame(record_details)
# separate keypoint features and HOG features apart
X1 = X.iloc[:, :50]
X2 = X.iloc[:, 50:]
# apply scaler to each type of feature
trans = StandardScaler()
X1 = trans.fit_transform(X1)
X2 = trans.fit_transform(X2)
# merge features after applying standard scaler
X = np.concatenate((X1, X2), axis=1)
# apply scaling to entire dataset
X = trans.fit_transform(X)
pose_preds = []
if isPCAon:
# load PCA model
pca = load('saved_models/SVM-PCA-transform.joblib')
# apply mapping (transform) to both training and test set
X = pca.transform(X)
# load SVM classifier with PCA
clf = load('saved_models/SVM-model-with-PCA.joblib')
# predict poses
pose_preds = clf.predict(X)
else:
# load SVM classifier without PCA
clf = load('saved_models/SVM-model-without-PCA.joblib')
# predict poses
pose_preds = clf.predict(X)
# get encoded classes
encoded_classes = load('saved_models/encoded-classes.joblib')
# build predict poses for each image
# there might be more than one pose in one image
image_poses_list = [()] * len(images_data)
for pose_idx in range(0, len(pose_preds)):
image_poses_list[img_idxs_of_records[pose_idx]] = image_poses_list[
img_idxs_of_records[pose_idx]] + (pose_preds[pose_idx], )
# count instances of poses in each image
# and build label for each predicted image
# hold labels for each predicted image
predicted_img_lbls = [None] * len(images_data)
for img_idx in range(0, len(image_poses_list)):
c = Counter(image_poses_list[img_idx])
lbl = ""
for pose in c.keys():
lbl = lbl + str(c[pose]) + " " + str(
encoded_classes[pose]) + ", "
if lbl == "": lbl = "No pose detected"
else: lbl = lbl.rstrip().rstrip(',')
# assign label to list
predicted_img_lbls[img_idx] = lbl
# show grid of predicted images
nCols = 4
nRows = 1
if len(images_data) % nCols == 0:
nRows = (int)(len(images_data) / nCols)
else:
nRows = (int)(len(images_data) / nCols + 1)
pyplot.rc('figure', figsize=(20, 20))
fig = pyplot.figure()
grid = ImageGrid(
fig,
111, # similar to subplot(111)
nrows_ncols=(nRows, nCols), # creates mxn grid of axes
axes_pad=0.3, # pad between axes in inch.
share_all=True)
fontdict = {'fontsize': 10, 'color': 'red'}
for ax, im, lbl in zip(grid, images_data, predicted_img_lbls):
# resize image
im = cv2.resize(im, (400, 320))
# Iterating over the grid returns the Axes.
ax.imshow(im)
ax.set_title(lbl, fontdict=fontdict, pad=2)
pyplot.show()
## if datum.poseKeypoints[p,i,2] < .70:
## color = (0,255,255)
## if datum.poseKeypoints[p,i,2] < .2:
## color = (0,0,255)
## cv2.circle(newImage, (datum.poseKeypoints[p,i,0],datum.poseKeypoints[p,i,1]),3,color,2)
##DrawPointsWithConfidence(newImage,datum.poseKeypoints)
##DrawLineOnImage(newImage,[1,-1,0])
##cv2.imshow("Confidence Points", newImage)
testbodypartID = 4
imagesToLoad = GetImagesToLoad()
datums = [op.Datum() for i in range(numberOfCameras)]
imagesToProcess = loadImages(imagesToLoad)
for i in range(numberOfCameras):
datums[i].cvInputData = imagesToProcess[i]
opWrapper.emplaceAndPop([datums[i]])
imgIDA = 2
imgIDB = 6
imgIDC = 4
imgIDD = 8
#newImage = imagesToProcess[imgIDA].copy()
#DrawPointsWithConfidence(newImage,datums[imgIDA].poseKeypoints)
#cv2.imshow("Confidence Points", newImage)
cameraIntrinsics = GetCameraIntrinsics(cameraIntrinsicsFileLocation,
numberOfCameras)
'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?'
)
raise e
params = dict()
params['model_folder'] = config.OPENPOSE_MODEL_PATH
params['hand'] = config.OPENPOSE_DETECT_HAND
params['face'] = config.OPENPOSE_DETECT_FACE
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
image = cv2.imread('G:\\images\\a-characters\\qi-sheng-luo-zbz2-0004.jpg')
height, width, _ = image.shape
print(f'raw shape: {height} x {width}')
min_axis = min(height, width)
height_ = 768 if height == min_axis else int(height * (768. / min_axis))
width_ = 768 if width == min_axis else int(width * (768. / min_axis))
datum.cvInputData = image
opWrapper.emplaceAndPop([datum])
keypoints = datum.poseKeypoints[0] if len(
datum.poseKeypoints) > 0 else None
if keypoints is None:
sys.exit(0)
print('key size:', len(keypoints))
x_list = [int(x[0]) for x in keypoints]
def main(session):
# Nao
motion_service = session.service("ALMotion")
posture_service = session.service("ALRobotPosture")
motion_service.wakeUp()
posture_service.goToPosture("Stand", 0.5)
params = dict()
params[
"model_folder"] = "/home/anton/Documents/hci-project/openpose/models"
# params["model_pose"] = "COCO"
params["net_resolution"] = "160x80"
params["body"] = 1
params["display"] = 1
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Videao
datum = op.Datum()
cam = cv2.VideoCapture(0) # modify here for camera number
while (cv2.waitKey(1) != 27):
# Get camera frame
# cam.set(cv2.CAP_PROP_BUFFERSIZE, 1)
ret, frame = cam.read()
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
frame = datum.cvOutputData
cv2.imshow("Webcam", frame) # datum.cvOutputData)
leftRight, theta, phi = [], [], []
print(len(str(datum.poseKeypoints)))
print("\n")
if len(str(datum.poseKeypoints)) > 1000:
if (datum.poseKeypoints[0][5][2] > 0.7
and datum.poseKeypoints[0][6][2] > 0.7
and datum.poseKeypoints[0][7][2] > 0.7):
theta.append(
math.atan2(
datum.poseKeypoints[0][6][1] -
datum.poseKeypoints[0][5][1],
datum.poseKeypoints[0][6][0] -
datum.poseKeypoints[0][5][0]))
leftRight.append("L")
print("Left - Theta: " + str(math.degrees(theta[-1])) + "\n")
if datum.poseKeypoints[0][2][2] > 0.7 and datum.poseKeypoints[0][3][2] > 0.7\
and datum.poseKeypoints[0][4][2] > 0.7:
theta.append(-math.atan2(
datum.poseKeypoints[0][2][1] - datum.poseKeypoints[0][3]
[1], datum.poseKeypoints[0][2][0] -
datum.poseKeypoints[0][3][0]))
leftRight.append("R")
print("Right - Theta: " + str(math.degrees(theta[-1])) + "\n")
userArmArticular(motion_service, theta, leftRight)
# Always clean up
cam.release()
cv2.destroyAllWindows()