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reverse_choreo_capture.py
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import cv2
import mediapipe as mp
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
import time
import sys
song_artist_pairs = {
'1':('thats_what_i_like','bruno_mars'),
'2':('humble','kendrick_lamar'),
'3':('skeletons','keshi'),
'4':('slow_dancing_in_the_dark','joji'),
'5':('lite_spots','kaytranada'),
'6':('woman','doja_cat'),
'7':('get_up','ciara'),
'8':('throwin_elbows','excision'),
'9':('power','little mix'),
'10':('peaches','justin_bieber'),
'11':('knife_talk','drake'),
'12':('fool_around','yas'),
'13':('levitating','dua_lipa'),
'14':('feed_the_fire','lucky_daye'),
'15':('easily','bruno_major'),
'16':('good_4_u','olivia_rodrigo'),
'17':('all_i_wanna_do','jay_park'),
'18':('sad_girlz_luv_money','amaarae'),
'19':('tik_tok','kesha'),
'20':('ymca','village_people'),
'21':('intuition_interlude','jamie_foxx'),
'22':('kilby_girl','the_backseat_lovers'),
'23':('party_rock_anthem','lmfao'),
'24':('frozen','sabrina_claudio'),
'25':('weve_never_met_but_can_we_have_a_cup_of_coffee_or_something','in_love_with_a_ghost'),
'26':('piano_song','eryn_allen_kane'),
'27':('tacones_rojos','sebastian_yatra'),
'28':('face_to_face','rex_orange_county'),
'29':('violin_concerto_no_1_in_a_minor','johann_sebastian_bach'),
'30':('candy','baekhyun'),
'31':('whisper','boombox_cartel'),
'32':('in_the_name_of_love','martin_garrix'),
'33':('brukka','jack_and_lewis'),
'34':('bum_bum_tam_tam','mc_fioti'),
'35':('partition','beyonce'),
'36':('greedy','ariana_grande'),
'37':('idontwannabeyouanymore','billie_eilish'),
'38':('lost_in_japan','shawn_mendes'),
'39':('the_saints','andy_mineo'),
'40':('dynamite','bts')
}
# run as 'python3 mediaposetest3.py [dancer_id] [song_artist_id]' e.g. 'python3 mediaposetest3.py 1 1'
def main():
args = sys.argv[1:]
dancer_id = args[0]
song_artist_id = args[1]
mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic
count = 0
alldata = []
fps_time = 0
start_time = time.time()
pose_tubuh = ['NOSE', 'LEFT_EYE_INNER', 'LEFT_EYE', 'LEFT_EYE_OUTER', 'RIGHT_EYE_INNER', 'RIGHT_EYE', 'RIGHT_EYE_OUTER', 'LEFT_EAR', 'RIGHT_EAR', 'MOUTH_LEFT', 'MOUTH_RIGHT',
'LEFT_SHOULDER', 'RIGHT_SHOULDER', 'LEFT_ELBOW', 'RIGHT_ELBOW', 'LEFT_WRIST', 'RIGHT_WRIST', 'LEFT_PINKY', 'RIGHT_PINKY', 'LEFT_INDEX', 'RIGHT_INDEX', 'LEFT_THUMB',
'RIGHT_THUMB', 'LEFT_HIP', 'RIGHT_HIP', 'LEFT_KNEE', 'RIGHT_KNEE', 'LEFT_ANKLE', 'RIGHT_ANKLE', 'LEFT_HEEL', 'RIGHT_HEEL', 'LEFT_FOOT_INDEX', 'RIGHT_FOOT_INDEX']
pose_tangan = ['WRIST', 'THUMB_CPC', 'THUMB_MCP', 'THUMB_IP', 'THUMB_TIP', 'INDEX_FINGER_MCP', 'INDEX_FINGER_PIP', 'INDEX_FINGER_DIP', 'INDEX_FINGER_TIP', 'MIDDLE_FINGER_MCP',
'MIDDLE_FINGER_PIP', 'MIDDLE_FINGER_DIP', 'MIDDLE_FINGER_TIP', 'RING_FINGER_PIP', 'RING_FINGER_DIP', 'RING_FINGER_TIP',
'RING_FINGER_MCP', 'PINKY_MCP', 'PINKY_PIP', 'PINKY_DIP', 'PINKY_TIP']
pose_tangan_2 = ['WRIST2', 'THUMB_CPC2', 'THUMB_MCP2', 'THUMB_IP2', 'THUMB_TIP2', 'INDEX_FINGER_MCP2', 'INDEX_FINGER_PIP2', 'INDEX_FINGER_DIP2', 'INDEX_FINGER_TIP2', 'MIDDLE_FINGER_MCP2',
'MIDDLE_FINGER_PIP2', 'MIDDLE_FINGER_DIP2', 'MIDDLE_FINGER_TIP2', 'RING_FINGER_PIP2', 'RING_FINGER_DIP2', 'RING_FINGER_TIP2',
'RING_FINGER_MCP2', 'PINKY_MCP2', 'PINKY_PIP2', 'PINKY_DIP2', 'PINKY_TIP2']
cap = cv2.VideoCapture(0)
suc,frame_video = cap.read()
vid_writer = cv2.VideoWriter('pose.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 10, (frame_video.shape[1], frame_video.shape[0]))
with mp_holistic.Holistic(
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as holistic:
while cap.isOpened():
success, image = cap.read()
end_time = time.time()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# Flip the image horizontally for a later selfie-view display, and convert
# the BGR image to RGB.
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
results = holistic.process(image)
# Draw landmark annotation on the image.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image_asli = np.copy(image)
image = np.zeros(image.shape)
mp_drawing.draw_landmarks(
image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
mp_drawing.draw_landmarks(
image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
mp_drawing.draw_landmarks(
image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
# if(results.pose_landmarks is not None and results.left_hand_landmarks is not None and results.right_hand_landmarks is not None):
if results.pose_landmarks:
data_tubuh = {}
for i in range(len(pose_tubuh)):
results.pose_landmarks.landmark[i].x = results.pose_landmarks.landmark[i].x * image.shape[0]
results.pose_landmarks.landmark[i].y = results.pose_landmarks.landmark[i].y * image.shape[1]
data_tubuh.update(
{pose_tubuh[i] : results.pose_landmarks.landmark[i]}
)
alldata.append(data_tubuh)
if results.right_hand_landmarks:
for i in range(len(pose_tangan)):
results.right_hand_landmarks.landmark[i].x = results.right_hand_landmarks.landmark[i].x * image.shape[0]
results.right_hand_landmarks.landmark[i].y = results.right_hand_landmarks.landmark[i].y * image.shape[1]
data_tubuh.update(
{pose_tangan[i] : results.right_hand_landmarks.landmark[i]}
)
alldata.append(data_tubuh)
if results.left_hand_landmarks:
for i in range(len(pose_tangan)):
results.left_hand_landmarks.landmark[i].x = results.left_hand_landmarks.landmark[i].x * image.shape[0]
results.left_hand_landmarks.landmark[i].y = results.left_hand_landmarks.landmark[i].y * image.shape[1]
data_tubuh.update(
{pose_tangan_2[i] : results.left_hand_landmarks.landmark[i]}
)
alldata.append(data_tubuh)
if results.pose_landmarks or results.right_hand_landmarks or results.left_hand_landmarks:
data_tubuh.update(
{'TIMESTAMP': end_time-start_time}
)
alldata.append(data_tubuh)
cv2.namedWindow('MediaPipe Holistic', cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty('MediaPipe Holistic', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2,)
cv2.imshow('MediaPipe Holistic', image) #sudah menampilkan backgrounnd hitam dan skeleton
cv2.imshow('Gambar asli', image_asli)
count = count + 1
print(count)
fps_time = time.time()
vid_writer.write(image)
#plt.imshow((image*255).astype(np.uint8))
#plt.savefig("image-frame/" + str(count) + ".jpg")
if (cv2.waitKey(5) & 0xFF == 27) or (end_time-start_time >= 25):
df = pd.DataFrame(alldata)
run_name = f'data/{dancer_id}_{song_artist_pairs[song_artist_id][0]}_{song_artist_pairs[song_artist_id][1]}.csv'
df.to_csv(run_name)
break
cap.release()
if __name__ == "__main__":
main()