def get_pose(video_file, output_path):
# data out
output_data = []
# Output location
output_format = '.mp4'
video = os.path.basename(video_file).split('.')[0]
video_output = output_path + '/' + video + str(start_datetime) + output_format
# Video reader
cam = cv2.VideoCapture(video_file)
input_fps = cam.get(cv2.CAP_PROP_FPS)
ret_val, orig_image = cam.read()
video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT))
ending_frame = video_length
# Video writer
output_fps = input_fps / frame_rate_ratio
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(video_output, fourcc, output_fps, (orig_image.shape[1], orig_image.shape[0]))
# scale_search = [1, .5, 1.5, 2] # [.5, 1, 1.5, 2]
# scale_search = scale_search[0:process_speed]
#
# params['scale_search'] = scale_search
i = 0 # default is 0
while(cam.isOpened()) and ret_val is True and i < ending_frame:
if i % frame_rate_ratio == 0:
input_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR)
tic = time.time()
# generate image with body parts
body_parts, all_peaks, subset, candidate = extract_parts(input_image, params, model, model_params)
output_data.append({'frame_id': i, 'body_parts': body_parts, 'all_peaks': all_peaks,
'subset': subset, 'candidate': candidate})
canvas = draw(orig_image, all_peaks, subset, candidate)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
out.write(canvas)
ret_val, orig_image = cam.read()
i += 1
return output_data
def std_main():
parser = argparse.ArgumentParser()
parser.add_argument('--image', type=str, required=True, help='input image')
parser.add_argument('--output',
type=str,
default='result.png',
help='output image')
parser.add_argument('--model',
type=str,
default='model/keras/model.h5',
help='path to the weights file')
args = parser.parse_args()
image_path = args.image
output = args.output
keras_weights_file = args.model
tic = time.time()
print('start processing...')
# load model
# authors of original model don't use
# vgg normalization (subtracting mean) on input images
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
input_image = cv2.imread(image_path) # B,G,R order
all_peaks, subset, candidate = extract_parts(input_image, params, model,
model_params)
canvas = draw(input_image, all_peaks, subset, candidate)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
cv2.imwrite(output, canvas)
cv2.destroyAllWindows()
# load model
# authors of original model don't use
# vgg normalization (subtracting mean) on input images
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
#immagine da classificare
input_image = cv2.imread('images.jpg') # B,G,R order
body_parts, all_peaks, subset, candidate = extract_parts(
input_image, params, model, model_params)
canvas, dict, lis1, lis2 = draw(input_image, all_peaks, subset, candidate)
cv2.imwrite(output, canvas)
Concatena.salva_csv_dist(lis1, lis2, 'none')
dataframe = pandas.read_csv("dataset_dist.csv")
dataset = dataframe.values
riga = dataframe.shape[0]
X = dataset[:, 0:93]
toc = time.time()
print('processing time is %.5f' % (toc - tic))
out = cv2.VideoWriter(video_output, fourcc, output_fps,
(orig_image.shape[1], orig_image.shape[0]))
scale_search = [1, .5, 1.5, 2] # [.5, 1, 1.5, 2]
scale_search = scale_search[0:process_speed]
params['scale_search'] = scale_search
i = 0 # default is 0
while (cam.isOpened()) and ret_val is True and i < ending_frame:
if i % frame_rate_ratio == 0:
input_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR)
tic = time.time()
# generate image with body parts
body_parts, all_peaks, subset, candidate = extract_parts(
input_image, params, model, model_params)
canvas = draw(orig_image, all_peaks, subset, candidate)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
out.write(canvas)
ret_val, orig_image = cam.read()
i += 1
output = args.output
keras_weights_file = args.model
tic = time.time()
print('start processing...')
# load model
# authors of original model don't use
# vgg normalization (subtracting mean) on input images
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
print("params", params, "\n")
print(output)
input_image = cv2.imread(image_path) # B,G,R order
all_peaks, subset, candidate = extract_parts(input_image, params, model,
model_params)
canvas = draw(input_image, all_peaks, subset, candidate)
toc = time.time()
cv2.imwrite(output, canvas)
cwd = os.getcwd()
print(all_peaks)
des_dir = cwd + '/RESULTS/'
with open((des_dir + 'parameters.data'), 'wb') as file:
pickle.dump(all_peaks, file)
cv2.destroyAllWindows()
def video_cap(Excersise):
path_model_h5 = 'model.h5'
keras_weights_file = path_model_h5
#Analysis for the every n frames
frame_rate_ratio = 1
#Int 1 (fastest, lowest quality) to 4 (slowest, highest quality)
process_speed = 1
#ending_frame = args.end
print('start processing...')
# Video input
video_file = '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/push.mp4'
print(video_file)
# Output location
video_output = '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/push4.avi'
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
# Video reader
#cam = cv2.VideoCapture(video_file)
cam = cv2.VideoCapture(video_file)
input_fps = cam.get(cv2.CAP_PROP_FPS)
print("input frames per second:-", input_fps)
ret_val, orig_image = cam.read()
video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT))
print("total frames in a video:-", video_length)
# Video writer
output_fps = input_fps / frame_rate_ratio
print("out put frames:-", output_fps)
frame_width = int(cam.get(3))
frame_height = int(cam.get(4))
print("width:-", frame_width)
print("Height:-", frame_height)
out = cv2.VideoWriter(video_output,
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
output_fps, (frame_width, frame_height))
#out = cv2.VideoWriter(video_output, cv2.VideoWriter_fourcc(*'DIVX'),output_fps, (frame_width, frame_height))
scale_search = [1, .5, 1.5, 2] # [.5, 1, 1.5, 2]
scale_search = scale_search[0:process_speed]
params['scale_search'] = scale_search
i = 0 # default is 0
count = 0
while (cam.isOpened()) and ret_val is True:
if ret_val is None:
break
if i % frame_rate_ratio == 0:
input_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR)
tic = time.time()
if Excersise == 'Normal':
all_peaks, subset, candidate = extract_parts(
input_image, params, model, model_params)
canvas, theta, theta1, theta2, theta3, Angle1, Angle2, Angle3, Angle4 = draw(
orig_image, all_peaks, subset, candidate)
cv2.rectangle(canvas, (0, 0), (265, 35),
color=(0, 255, 0),
thickness=2)
cv2.putText(canvas,
"right Hand angle :- {0:.2f}".format(float(theta)),
(30, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255))
cv2.putText(canvas,
"right leg angle :- {0:.2f}".format(float(theta2)),
(30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255))
cv2.rectangle(canvas, (645, 0), (900, 35),
color=(0, 255, 0),
thickness=2)
cv2.putText(canvas,
"left Hand angle :- {0:.2f}".format(float(theta1)),
(650, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 225))
cv2.putText(canvas,
"left leg angle :- {0:.2f}".format(float(theta3)),
(650, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255))
elif Excersise == 'Fat':
all_peaks1, subset1, candidate1 = extract_parts_angle(
input_image, params, model, model_params)
canvas, theta5, theta6, Angle5, Angle6 = draw_angle(
orig_image, all_peaks1, subset1, candidate1)
cv2.rectangle(canvas, (645, 0), (900, 35),
color=(0, 255, 0),
thickness=2)
cv2.putText(canvas, "Left :- {0:.2f}".format(float(theta5)),
(650, 15), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 225))
cv2.putText(canvas, "Right :- {0:.2f}".format(float(theta6)),
(650, 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255))
elif Excersise == 'Pushups':
print("HIIIIIII")
all_peaks2, subset2, candidate2 = extract_parts_push(
input_image, params, model, model_params)
canvas, theta7, theta8, Angle7, Angle8 = draw_push(
orig_image, all_peaks2, subset2, candidate2)
print("THeta 7, theta8 ", theta7, theta8)
if float(theta7) > 170.0 or float(theta8) > 170.0:
count = count + 1
cv2.rectangle(canvas, (645, 0), (900, 60),
color=(255, 25, 100),
thickness=2)
cv2.putText(canvas,
"Left :- {0:.2f}".format(float(theta7)),
(650, 30), cv2.FONT_HERSHEY_DUPLEX, 1,
(0, 0, 225))
cv2.putText(canvas,
"Right :- {0:.2f}".format(float(theta8)),
(650, 45), cv2.FONT_HERSHEY_DUPLEX, 1,
(0, 0, 255))
cv2.putText(canvas,
f"count :- {count}".format(float(theta8)),
(300, 35), cv2.FONT_HERSHEY_DUPLEX, 1,
(0, 0, 255))
else:
cv2.rectangle(canvas, (645, 0), (900, 60),
color=(255, 25, 100),
thickness=2)
cv2.putText(canvas,
"Left :- {0:.2f}".format(float(theta7)),
(650, 30), cv2.FONT_HERSHEY_DUPLEX, 1,
(0, 255, 0))
cv2.putText(canvas,
"Right :- {0:.2f}".format(float(theta8)),
(650, 45), cv2.FONT_HERSHEY_DUPLEX, 1,
(0, 255, 0))
else:
print("Sorry Wrong Excersise was given")
print('Processing frame: ', i)
toc = time.time()
out.write(canvas)
ret_val, orig_image = cam.read()
i += 1
if cv2.waitKey(25) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
cam.release()
convert_video(
'/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/pose14.avi',
'/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/pose14.mp4'
)
return Angle1, Angle2, Angle3, Angle4
break
if mirror:
orig_image = cv2.flip(orig_image, 1)
tic = time.time()
cropped = crop(orig_image, width, factor)
input_image = cv2.resize(cropped, (0, 0), fx=1/resize_fac, fy=1/resize_fac, interpolation=cv2.INTER_CUBIC)
input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
# generate image with body parts
body_parts, all_peaks, subset, candidate = extract_parts(input_image, params, model, model_params)
canvas = draw(cropped, all_peaks, subset, candidate, resize_fac=resize_fac)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
if out is not None:
out.write(canvas)
# canvas = cv2.resize(canvas, (0, 0), fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
cv2.imshow('frame', canvas)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def cameraLoad():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
type=int,
default=0,
help='ID of the device to open') #parser에서 받는거
parser.add_argument('--model',
type=str,
default='model/keras/model.h5',
help='path to the weights file') #모델경로
parser.add_argument('--frame_ratio',
type=int,
default=7,
help='analyze every [n] frames')
# --process_speed changes at how many times the model analyzes each frame at a different scale
parser.add_argument(
'--process_speed',
type=int,
default=1,
help=
'Int 1 (fastest, lowest quality) to 4 (slowest, highest quality)')
parser.add_argument('--out_name',
type=str,
default=None,
help='name of the output file to write')
parser.add_argument('--mirror',
type=bool,
default=True,
help='whether to mirror the camera')
# 받은 값들 저장하는것
args = parser.parse_args()
device = args.device
keras_weights_file = args.model
frame_rate_ratio = args.frame_ratio
process_speed = args.process_speed
out_name = args.out_name
mirror = args.mirror
print('start processing...')
# load model
# authors of original model don't use
# vgg normalization (subtracting mean) on input images
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
# Video reader
cam = cv2.VideoCapture(device) # 디바이스=0은 카메라, 파일넣고싶으면 파일 경로 넣으면 됨
# CV_CAP_PROP_FPS
input_fps = cam.get(cv2.CAP_PROP_FPS) # 해당카메라의 프레임으로 추측
print("Running at {} fps.".format(input_fps))
ret_val, orig_image = cam.read(
) #ret = 제대로 read됐는지확인하는 부울타입, orig = 실질적인 프레임단위의 이미지
width = orig_image.shape[1]
height = orig_image.shape[0]
factor = 0.3
out = None
# Output location
if out_name is not None and ret_val is not None: # out_name이 parser 40번째줄/ out_name이 잇으면 파일생성/
output_path = 'videos/outputs/'
output_format = '.mp4'
video_output = output_path + out_name + output_format
# Video writer
output_fps = input_fps / frame_rate_ratio
tmp = crop(orig_image, width, factor)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(video_output, fourcc, output_fps,
(tmp.shape[1], tmp.shape[0]))
del tmp
scale_search = [0.22, 0.25, .5, 1, 1.5, 2] # [.5, 1, 1.5, 2]
scale_search = scale_search[0:process_speed]
params['scale_search'] = scale_search
i = 0 # default is 0
resize_fac = 8
# while(cam.isOpened()) and ret_val is True:
while True:
cv2.waitKey(10)
if cam.isOpened() is False or ret_val is False:
break
if mirror:
orig_image = cv2.flip(orig_image, 1)
tic = time.time()
cropped = crop(orig_image, width, factor) # 파일 자름
#opencv함수로 사이즈 조절하는 것
input_image = cv2.resize(cropped, (0, 0),
fx=1 / resize_fac,
fy=1 / resize_fac,
interpolation=cv2.INTER_CUBIC)
input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
# generate image with body parts
# extract_part
all_peaks, subset, candidate = extract_parts(
input_image, params, model, model_params)
canvas = draw(cropped,
all_peaks,
subset,
candidate,
resize_fac=resize_fac)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
if out is not None: # 이게 있으면 계속 출력하는 것
out.write(canvas) # 이미지 위에 만들어진 스켈레톤!
# canvas = cv2.resize(canvas, (0, 0), fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
cv2.imshow('frame', canvas)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ret_val, orig_image = cam.read()
i += 1
cropped = crop(orig_image, width, factor)
input_image = cv2.resize(cropped, (0, 0),
fx=1 / resize_fac,
fy=1 / resize_fac,
interpolation=cv2.INTER_CUBIC)
input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
# generate image with body parts
body_parts, all_peaks, subset, candidate = extract_parts(
input_image, params, model, model_params)
canvas, dict, lis1, lis2 = draw(cropped,
all_peaks,
subset,
candidate,
resize_fac=resize_fac)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
Concatena.salva_csv_dist(lis1, lis2, 'none')
dataframe = pandas.read_csv("dataset_dist.csv")
dataset = dataframe.values
riga = dataframe.shape[0]
input_image = cv2.resize(cropped, (0, 0),
fx=1 / resize_fac,
fy=1 / resize_fac,
interpolation=cv2.INTER_CUBIC)
input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
print(cropped.shape, input_image.shape)
# generate image with body parts
all_peaks, subset, candidate = extract_parts(input_image, params,
model, model_params)
canvas = draw(cropped,
all_peaks,
subset,
candidate,
resize_fac=resize_fac)
# add remove background only skeleton
numpy_tmp = np.zeros((canvas.shape[0], canvas.shape[1], 3),
dtype=np.uint8)
canvas_test = draw(numpy_tmp,
all_peaks,
subset,
candidate,
resize_fac=resize_fac)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
