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import mediapipe as mp
import numpy as np
import cv2
import copy
mp_holistic = mp.solutions.holistic
mp_drawing = mp.solutions.drawing_utils
width, height = 640, 480
model = mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5)
def mediapipe_detection(image):
# từ image, model dự đoán trả về kết quả (định dạng mặc định)
results = model.process(image)
return results
def extract_keypoint(results,last):
res = []
if results.pose_landmarks:
for p in results.pose_landmarks.landmark:
res.append(np.array([p.x,p.y,p.z,p.visibility]))
else:
for _ in range(33):
res.append(np.array([0,0,0,0]))
#--------------
if results.left_hand_landmarks:
for p in results.left_hand_landmarks.landmark:
res.append(np.array([p.x,p.y,p.z]))
elif last!= None and last.left_hand_landmarks:
for p in last.left_hand_landmarks.landmark:
res.append(np.array([p.x,p.y,p.z]))
else:
for _ in range(21):
res.append(np.array([0,0,0]))
#---------------
if results.right_hand_landmarks:
for p in results.right_hand_landmarks.landmark:
res.append(np.array([p.x,p.y,p.z]))
elif last!=None and last.right_hand_landmarks:
for p in last.right_hand_landmarks.landmark:
res.append(np.array([p.x,p.y,p.z]))
else:
for _ in range(21):
res.append(np.array([0,0,0]))
return res
def normalize_keypoint(res,img=None):
#normalize keypoint
x1,y1,x2,y2 = res[11][0]*width,res[11][1]*height,res[12][0]*width,res[12][1]*height
try:
cv2.circle(img,(int(x1),int(y1)),4,(0,255,255),-1)
cv2.circle(img,(int(x2),int(y2)),4,(0,255,255),-1)
except:
# print("No img found")
pass
dis = np.sqrt((x1-x2)**2+(y1-y2)**2)
x_cen = (res[11][0]+res[12][0])/2
y_cen = (res[11][1]+res[12][1])/2
vector = [0.5-x_cen,0.5-y_cen]
scale = (200*width/640)/dis
for i in range(len(res)):
if res[i][0]==0 and res[i][1]==0:
continue
res[i][0] = vector[0]+res[i][0]
res[i][1] = vector[1]+res[i][1]
res[i][0] = 0.5+(res[i][0]-0.5)*scale
res[i][1] = 0.5+(res[i][1]-0.5)*scale
return res
def update_mpresult(res,results,last):
c = 0
if results.pose_landmarks:
for p in results.pose_landmarks.landmark:
p.x = res[c][0]
p.y = res[c][1]
if(c==20 and p.y>1.1 and last): last.right_hand_landmarks = None
elif(c==19 and p.y>1.1 and last): last.left_hand_landmarks = None
c+=1
else:
for _ in range(33):
c+=1
if results.left_hand_landmarks:
for p in results.left_hand_landmarks.landmark:
p.x = res[c][0]
p.y = res[c][1]
c+=1
else:
if last!=None and last.left_hand_landmarks: results.left_hand_landmarks = copy.deepcopy(last.left_hand_landmarks)
for _ in range(21):
c+=1
if results.right_hand_landmarks:
for p in results.right_hand_landmarks.landmark:
p.x = res[c][0]
p.y = res[c][1]
c+=1
else:
if last!=None and last.right_hand_landmarks: results.right_hand_landmarks = copy.deepcopy(last.right_hand_landmarks)
for _ in range(21):
c+=1
return results
def extract_keypoints_flatten(result, last, img=None):
#đây là hàm chính thức
res = extract_keypoint(result, last)
res = normalize_keypoint(res,img)
update_mpresult(res,result,last)
return np.concatenate([x for x in res])
def mediapipe_process(frames):
"""Main function to call, process a batch of frames into numpy array for prediction"""
sequence = []
last = None
for frame in frames:
results = mediapipe_detection(frame)
keypoints = extract_keypoints_flatten(results, last)
last = copy.deepcopy(results)
sequence.append(keypoints)
return np.array(sequence) |