import time
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.InteractiveSession(config=config)
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../models/"
mpose = keras.models.load_model('weights-improvement-109-0.98.hdf5',custom_objects={'binary_precision':keras_metrics.precision(), 'binary_recall':keras_metrics.recall()})
poseModel = op.PoseModel.BODY_25
original_keypoints_index = op.getPoseBodyPartMapping(poseModel)
keypoints_index = dict((bp, num) for num, bp in original_keypoints_index.items())
vs = cv2.VideoCapture(0)
# Starting OpenPoseasdasdas
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
datum = op.Datum()
fps_time = 0
while True:
def getPoseBodyPartMapping(poseModel):
return pyopenpose.getPoseBodyPartMapping(poseModel)
def main():
try:
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.).")
#parser.add_argument("--image_dir", default="../openpose/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/"
params["model_folder"] = "../openpose/models/"
#params["number_people_max"] = 1
#save data as json to folder
#Find a better way to do this. Currently saves each frame as json
params["write_json"] = "json_output"
# 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
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
#Video location as a string
vid_location = "media/side_landscape_2.mp4"
#vid_location = "video.avi"
cap = cv2.VideoCapture(vid_location)
width = cap.get(3)
height = cap.get(4)
fps = cap.get(5)
font = cv2.FONT_HERSHEY_SIMPLEX
#Find Video Format
video_type = vid_location.split(".")[-1]
if video_type == "mp4":
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('media/output.mp4', fourcc, fps,
(int(width), int(height)))
elif video_type == "avi":
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('media/output.avi', fourcc, fps,
(int(width), int(height)))
else:
print("Video format not supported")
sys.exit(-1)
frame_num = 0
sway_tot = 0
unsuccessful_frames = 0
while (cap.isOpened()):
ret, frame = cap.read()
#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if ret == True:
# Process Image
datum = op.Datum()
imageToProcess = frame
frame_num += 1
cv2.putText(imageToProcess, str(frame_num), (100, 100), font,
1, (255, 255, 255), 1)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
# Display Image
print("Body keypoints: \n" + str(datum.poseKeypoints))
cv2.imshow("OpenPose 1.5.1 - Tutorial Python API",
datum.cvOutputData)
#Get x difference/sway between nose[0]/neck[1] with midhip[8]
#nose_x = datum.poseKeypoints[0][0][0]
#neck_x = datum.poseKeypoints[0][1][0]
#midhip_x = datum.poseKeypoints[0][8][0]
# part not found
# maybe change to confidence level == 0 because technically part could just be on left edge
# if (nose_x == 0 or neck_x == 0 or midhip_x == 0):
# unsuccessful_frames += 1
# print("A part not found- frame not used")
# else:
# print("nose", nose_x)
# print("neck", neck_x)
# print("midhip", midhip_x)
# sway = abs(nose_x - midhip_x)
# print("frame sway", sway)
# sway_tot += sway
# Save frame to output video
out.write(datum.cvOutputData)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# sway_avg = sway_tot/(frame_num - unsuccessful_frames)
# print("sway average", int(sway_avg))
#prints model part numbers
poseModel = op.PoseModel.BODY_25
print(op.getPoseBodyPartMapping(poseModel))
cap.release()
out.release()
cv2.destroyAllWindows
except Exception as e:
print(e)
sys.exit(-1)
def main(video_path, save_path):
for video in os.listdir(video_path):
# if video.endswith('.avi'):
if video.endswith('.mp4'):
print('Processing' + video + '...........')
vs = cv2.VideoCapture(video_path + '/' + video)
width = 1280
height = 720
vs.set(cv2.CAP_PROP_FRAME_WIDTH, width)
vs.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "../../../../models/"
params["model_pose"] = "BODY_25"
params["fps_max"] = -1
params['write_video_fps'] = -1
params['number_people_max'] = 1
poseModel = op.PoseModel.BODY_25
original_keypoints_index = op.getPoseBodyPartMapping(poseModel)
# keypoints_index = dict((bp, num) for num, bp in original_keypoints_index.items())
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Create objects to process pictures
datum = op.Datum()
# Create array to save all keypoint frame
KeypointFrame = np.array([])
image_count = 0
start = time.time()
while vs.isOpened():
#Get frame from video or webcam
ret, frame = vs.read()
if not ret:
break
#Give inputData for openpoes to process
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
#Check openpose whether detect keypoints or not
if datum.poseKeypoints.any() and datum.poseKeypoints.ndim == 3:
#Reshape keypoints data and save KeypointFrame
keypoints = datum.poseKeypoints[0].reshape(1, 25, 3)
if KeypointFrame.size != 0:
KeypointFrame = np.vstack((KeypointFrame, keypoints))
else:
KeypointFrame = keypoints
#Get openpose Output
# image = datum.cvOutputData
image_count += 1
#Show the output
#cv2.imshow("Openpose", image)
if cv2.waitKey(1) == ord('q'):
break
end = time.time()
total_time = end - start
print('FPS:', image_count / total_time)
#Save data as Numpy type
# np.save(save_path+'/'+video.replace(".mp4",".npy"),KeypointFrame)
np.save(save_path + '/' + video.replace(".mp4", ".npy"),
KeypointFrame)
print('Successful get ' + video + ' KeyPoints')
vs.release()
cv2.destroyAllWindows()