-
Notifications
You must be signed in to change notification settings - Fork 249
/
HandTrackingModule.py
195 lines (172 loc) · 7.17 KB
/
HandTrackingModule.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
"""
Hand Tracking Module
By: Computer Vision Zone
Website: https://www.computervision.zone/
"""
import cv2
import mediapipe as mp
import math
class HandDetector:
"""
Finds Hands using the mediapipe library. Exports the landmarks
in pixel format. Adds extra functionalities like finding how
many fingers are up or the distance between two fingers. Also
provides bounding box info of the hand found.
"""
def __init__(self, mode=False,maxHands=2, detectionCon=0.5, minTrackCon=0.5):
"""
:param mode: In static mode, detection is done on each image: slower
:param maxHands: Maximum number of hands to detect
:param detectionCon: Minimum Detection Confidence Threshold
:param minTrackCon: Minimum Tracking Confidence Threshold
"""
self.mode = mode
self.maxHands = maxHands
self.detectionCon = detectionCon
self.minTrackCon = minTrackCon
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(static_image_mode=self.mode, max_num_hands=self.maxHands,
min_detection_confidence=self.detectionCon, min_tracking_confidence = self.minTrackCon)
self.mpDraw = mp.solutions.drawing_utils
self.tipIds = [4, 8, 12, 16, 20]
self.fingers = []
self.lmList = []
def findHands(self, img, draw=True, flipType=True):
"""
Finds hands in a BGR image.
:param img: Image to find the hands in.
:param draw: Flag to draw the output on the image.
:return: Image with or without drawings
"""
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.results = self.hands.process(imgRGB)
allHands = []
h, w, c = img.shape
if self.results.multi_hand_landmarks:
for handType,handLms in zip(self.results.multi_handedness,self.results.multi_hand_landmarks):
myHand={}
## lmList
mylmList = []
xList = []
yList = []
for id, lm in enumerate(handLms.landmark):
px, py = int(lm.x * w), int(lm.y * h)
mylmList.append([px, py])
xList.append(px)
yList.append(py)
## bbox
xmin, xmax = min(xList), max(xList)
ymin, ymax = min(yList), max(yList)
boxW, boxH = xmax - xmin, ymax - ymin
bbox = xmin, ymin, boxW, boxH
cx, cy = bbox[0] + (bbox[2] // 2), \
bbox[1] + (bbox[3] // 2)
myHand["lmList"] = mylmList
myHand["bbox"] = bbox
myHand["center"] = (cx, cy)
if flipType:
if handType.classification[0].label =="Right":
myHand["type"] = "Left"
else:
myHand["type"] = "Right"
else:myHand["type"] = handType.classification[0].label
allHands.append(myHand)
## draw
if draw:
self.mpDraw.draw_landmarks(img, handLms,
self.mpHands.HAND_CONNECTIONS)
cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
(255, 0, 255), 2)
cv2.putText(img,myHand["type"],(bbox[0] - 30, bbox[1] - 30),cv2.FONT_HERSHEY_PLAIN,
2,(255, 0, 255),2)
if draw:
return allHands,img
else:
return allHands
def fingersUp(self,myHand):
"""
Finds how many fingers are open and returns in a list.
Considers left and right hands separately
:return: List of which fingers are up
"""
myHandType =myHand["type"]
myLmList = myHand["lmList"]
if self.results.multi_hand_landmarks:
fingers = []
# Thumb
if myHandType == "Right":
if myLmList[self.tipIds[0]][0] > myLmList[self.tipIds[0] - 1][0]:
fingers.append(1)
else:
fingers.append(0)
else:
if myLmList[self.tipIds[0]][0] < myLmList[self.tipIds[0] - 1][0]:
fingers.append(1)
else:
fingers.append(0)
# 4 Fingers
for id in range(1, 5):
if myLmList[self.tipIds[id]][1] < myLmList[self.tipIds[id] - 2][1]:
fingers.append(1)
else:
fingers.append(0)
return fingers
def findDistance(self,p1, p2, img=None):
"""
Find the distance between two landmarks based on their
index numbers.
:param p1: Point1
:param p2: Point2
:param img: Image to draw on.
:param draw: Flag to draw the output on the image.
:return: Distance between the points
Image with output drawn
Line information
"""
x1, y1 = p1
x2, y2 = p2
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
length = math.hypot(x2 - x1, y2 - y1)
info = (x1, y1, x2, y2, cx, cy)
if img is not None:
cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
return length,info, img
else:
return length, info
def main():
cap = cv2.VideoCapture(0)
detector = HandDetector(detectionCon=0.8, maxHands=2)
while True:
# Get image frame
success, img = cap.read()
# Find the hand and its landmarks
hands, img = detector.findHands(img) # with draw
# hands = detector.findHands(img, draw=False) # without draw
if hands:
# Hand 1
hand1 = hands[0]
lmList1 = hand1["lmList"] # List of 21 Landmark points
bbox1 = hand1["bbox"] # Bounding box info x,y,w,h
centerPoint1 = hand1['center'] # center of the hand cx,cy
handType1 = hand1["type"] # Handtype Left or Right
fingers1 = detector.fingersUp(hand1)
if len(hands) == 2:
# Hand 2
hand2 = hands[1]
lmList2 = hand2["lmList"] # List of 21 Landmark points
bbox2 = hand2["bbox"] # Bounding box info x,y,w,h
centerPoint2 = hand2['center'] # center of the hand cx,cy
handType2 = hand2["type"] # Hand Type "Left" or "Right"
fingers2 = detector.fingersUp(hand2)
# Find Distance between two Landmarks. Could be same hand or different hands
length, info, img = detector.findDistance(lmList1[8], lmList2[8], img) # with draw
# length, info = detector.findDistance(lmList1[8], lmList2[8]) # with draw
# Display
cv2.imshow("Image", img)
cv2.waitKey(1)
if __name__ == "__main__":
main()