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HandTrackingModule.py
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223 lines (188 loc) · 8.9 KB
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"""
Hand Tracking Module
By: Computer Vision Zone
Website: https://www.computervision.zone/
"""
import math
import cv2
import mediapipe as mp
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, staticMode=False, maxHands=2, modelComplexity=1, 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 modelComplexity: Complexity of the hand landmark model: 0 or 1.
:param detectionCon: Minimum Detection Confidence Threshold
:param minTrackCon: Minimum Tracking Confidence Threshold
"""
self.staticMode = staticMode
self.maxHands = maxHands
self.modelComplexity = modelComplexity
self.detectionCon = detectionCon
self.minTrackCon = minTrackCon
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(static_image_mode=self.staticMode,
max_num_hands=self.maxHands,
model_complexity=modelComplexity,
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, pz = int(lm.x * w), int(lm.y * h), int(lm.z * w)
mylmList.append([px, py, pz])
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)
return allHands, img
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
"""
fingers = []
myHandType = myHand["type"]
myLmList = myHand["lmList"]
if self.results.multi_hand_landmarks:
# 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, color=(255, 0, 255), scale=5):
"""
Find the distance between two landmarks input should be (x1,y1) (x2,y2)
:param p1: Point1 (x1,y1)
:param p2: Point2 (x2,y2)
:param img: Image to draw output on. If no image input output img is None
: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), scale, color, cv2.FILLED)
cv2.circle(img, (x2, y2), scale, color, cv2.FILLED)
cv2.line(img, (x1, y1), (x2, y2), color, max(1, scale // 3))
cv2.circle(img, (cx, cy), scale, color, cv2.FILLED)
return length, info, img
def main():
# Initialize the webcam to capture video
# The '2' indicates the third camera connected to your computer; '0' would usually refer to the built-in camera
cap = cv2.VideoCapture(0)
# Initialize the HandDetector class with the given parameters
detector = HandDetector(staticMode=False, maxHands=2, modelComplexity=1, detectionCon=0.5, minTrackCon=0.5)
# Continuously get frames from the webcam
while True:
# Capture each frame from the webcam
# 'success' will be True if the frame is successfully captured, 'img' will contain the frame
success, img = cap.read()
# Find hands in the current frame
# The 'draw' parameter draws landmarks and hand outlines on the image if set to True
# The 'flipType' parameter flips the image, making it easier for some detections
hands, img = detector.findHands(img, draw=True, flipType=True)
# Check if any hands are detected
if hands:
# Information for the first hand detected
hand1 = hands[0] # Get the first hand detected
lmList1 = hand1["lmList"] # List of 21 landmarks for the first hand
bbox1 = hand1["bbox"] # Bounding box around the first hand (x,y,w,h coordinates)
center1 = hand1['center'] # Center coordinates of the first hand
handType1 = hand1["type"] # Type of the first hand ("Left" or "Right")
# Count the number of fingers up for the first hand
fingers1 = detector.fingersUp(hand1)
print(f'H1 = {fingers1.count(1)}', end=" ") # Print the count of fingers that are up
# Calculate distance between specific landmarks on the first hand and draw it on the image
length, info, img = detector.findDistance(lmList1[8][0:2], lmList1[12][0:2], img, color=(255, 0, 255),
scale=10)
# Check if a second hand is detected
if len(hands) == 2:
# Information for the second hand
hand2 = hands[1]
lmList2 = hand2["lmList"]
bbox2 = hand2["bbox"]
center2 = hand2['center']
handType2 = hand2["type"]
# Count the number of fingers up for the second hand
fingers2 = detector.fingersUp(hand2)
print(f'H2 = {fingers2.count(1)}', end=" ")
# Calculate distance between the index fingers of both hands and draw it on the image
length, info, img = detector.findDistance(lmList1[8][0:2], lmList2[8][0:2], img, color=(255, 0, 0),
scale=10)
print(" ") # New line for better readability of the printed output
# Display the image in a window
cv2.imshow("Image", img)
# Keep the window open and update it for each frame; wait for 1 millisecond between frames
cv2.waitKey(1)
if __name__ == "__main__":
main()