Machine Vision Technology Development
Experiment 3 - Gesture Recognition Experience
Step 1: System Preparation
sudo apt update && sudo apt upgrade -ysudo apt install -y build-essential cmake pkg-config python3-dev python3-pip (can skip if python3 is already installed)Step 2: Create Virtual Environment
cd OPENCV
python3 -m venv rdkx5_vision_env
source rdkx5_vision_env/bin/activateStep 3: Install Dependencies
pip install --upgrade pip
pip install -r requirements.txtStep 4: Test Environment
python3 mediapipe_gesture_demo.py
Sample program includes the following features:
- ✅ Real-time gesture detection - supports simultaneous detection of both hands
- ✅ Number gesture recognition - recognizes finger counts 1-5
- ✅ Special gesture recognition - OK gesture, thumbs up gesture
- ✅ Performance monitoring - real-time FPS display
- ✅ Visual feedback - hand keypoint drawing
import cv2
import numpy as np
import math
import time
# Initialize camera
cap = cv2.VideoCapture(0)
# Set window size
window_width = 1280
window_height = 720
# Adjust camera resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, window_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, window_height)
# Create window
cv2.namedWindow('Hand Gesture Recognition', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Hand Gesture Recognition', window_width, window_height)
# Create slider window for adjusting skin color threshold
cv2.namedWindow('Skin Detection Controls')
cv2.resizeWindow('Skin Detection Controls', 400, 250)
# Create HSV threshold sliders for skin detection
cv2.createTrackbar('H_min', 'Skin Detection Controls', 0, 179, lambda x: None)
cv2.createTrackbar('H_max', 'Skin Detection Controls', 20, 179, lambda x: None)
cv2.createTrackbar('S_min', 'Skin Detection Controls', 30, 255, lambda x: None)
cv2.createTrackbar('S_max', 'Skin Detection Controls', 150, 255, lambda x: None)
cv2.createTrackbar('V_min', 'Skin Detection Controls', 60, 255, lambda x: None)
cv2.createTrackbar('V_max', 'Skin Detection Controls', 255, 255, lambda x: None)
# Set default values
cv2.setTrackbarPos('H_min', 'Skin Detection Controls', 0)
cv2.setTrackbarPos('H_max', 'Skin Detection Controls', 20)
cv2.setTrackbarPos('S_min', 'Skin Detection Controls', 30)
cv2.setTrackbarPos('S_max', 'Skin Detection Controls', 150)
cv2.setTrackbarPos('V_min', 'Skin Detection Controls', 60)
cv2.setTrackbarPos('V_max', 'Skin Detection Controls', 255)
# Function to count fingers
def count_fingers(contour, drawing):
# Calculate convex hull
hull = cv2.convexHull(contour, returnPoints=False)
# If too few hull points, cannot calculate defects
if len(hull) < 3:
return 0
# Calculate convexity defects
defects = cv2.convexityDefects(contour, hull)
if defects is None:
return 0
# Count valid convexity defects (gaps between fingers)
finger_count = 0
for i in range(defects.shape[0]):
s, e, f, d = defects[i, 0]
start = tuple(contour[s][0])
end = tuple(contour[e][0])
far = tuple(contour[f][0])
# Calculate three sides of triangle
a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)
b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2)
c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2)
# Use cosine theorem to calculate angle
angle = math.degrees(math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)))
# If angle is less than 90 degrees, consider it as gap between fingers
if angle <= 90:
# Mark defect point on image
cv2.circle(drawing, far, 5, [0, 0, 255], -1)
finger_count += 1
# Defect count plus 1 equals finger count (because defects are gaps between fingers)
return finger_count + 1
# Main loop
while cap.isOpened():
success, image = cap.read()
if not success:
print("Unable to get camera frame")
break
# Flip image horizontally to look more like a mirror
image = cv2.flip(image, 1)
# Create a copy for drawing
drawing = image.copy()
# Convert to HSV color space
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Get current skin color threshold
h_min = cv2.getTrackbarPos('H_min', 'Skin Detection Controls')
h_max = cv2.getTrackbarPos('H_max', 'Skin Detection Controls')
s_min = cv2.getTrackbarPos('S_min', 'Skin Detection Controls')
s_max = cv2.getTrackbarPos('S_max', 'Skin Detection Controls')
v_min = cv2.getTrackbarPos('V_min', 'Skin Detection Controls')
v_max = cv2.getTrackbarPos('V_max', 'Skin Detection Controls')
# Create skin color mask
lower_skin = np.array([h_min, s_min, v_min])
upper_skin = np.array([h_max, s_max, v_max])
mask = cv2.inRange(hsv, lower_skin, upper_skin)
# Perform morphological operations to remove noise
kernel = np.ones((5, 5), np.uint8)
mask = cv2.erode(mask, kernel, iterations=1)
mask = cv2.dilate(mask, kernel, iterations=2)
mask = cv2.GaussianBlur(mask, (5, 5), 0)
# Find contours
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Find the largest contour (assumed to be hand)
if contours:
max_contour = max(contours, key=cv2.contourArea)
# Only process contours large enough
if cv2.contourArea(max_contour) > 5000:
# Draw contour
cv2.drawContours(drawing, [max_contour], 0, (0, 255, 0), 2)
# Calculate and display finger count
finger_count = count_fingers(max_contour, drawing)
# Limit finger count between 1-5
finger_count = max(1, min(5, finger_count))
# Display number on image
cv2.putText(
drawing,
f"Fingers: {finger_count}",
(50, 50),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 255, 0),
2,
cv2.LINE_AA
)
# Display skin detection result
cv2.imshow('Skin Detection', mask)
# Display final result
cv2.imshow('Hand Gesture Recognition', drawing)
# Display usage instructions
cv2.putText(
drawing,
"Adjust sliders to detect skin color properly",
(10, drawing.shape[0] - 40),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 255),
1,
cv2.LINE_AA
)
cv2.putText(
drawing,
"Press 'q' to quit",
(10, drawing.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 255),
1,
cv2.LINE_AA
)
# Press 'q' to quit
if cv2.waitKey(5) & 0xFF == ord('q'):
break
# Release resources
cap.release()
cv2.destroyAllWindows()