3 篇博文 含有标签「手势识别」

# Python端代码
fifo = open('/tmp/gesture_fifo', 'wb')
while True:
    _, jpeg = cv2.imencode('.jpg', processed_frame,
                [cv2.IMWRITE_JPEG_QUALITY, 85])
    fifo.write(jpeg.tobytes())
    fifo.flush()

def restart_3a(self):
    os.system("killall rkaiq_3A_server 2>/dev/null")
    time.sleep(2)
    os.system("rm -f /tmp/.rkaiq_3A* 2>/dev/null")
    os.system("/etc/init.d/S40rkaiq_3A start >/dev/null 2>&1")
    time.sleep(5)

# 检测器选择逻辑
if USE_CVZONE:
    detector = CvzoneHandDetector(detectionCon=0.8, maxHands=1)
elif USE_MEDIAPIPE:
    detector = MediapipeHandDetector(maxHands=1,
                    detectionCon=0.5,
                    drawLandmarks=False)
else:
    detector = SimpleHandDetector()  # HSV备用方案

class MediapipeHandDetector:
    def findHands(self, frame, flipType=False):
        img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = self.hands.process(img_rgb)
        
        hands = []
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                # 提取21个关键点坐标
                lmList = []
                for lm in hand_landmarks.landmark:
                    x_px = int(lm.x * width)
                    y_px = int(lm.y * height)
                    lmList.append([x_px, y_px, lm.z])
            
                hands.append({'lmList': lmList})
        
        return hands, frame

def detect_hand_simple(frame):
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, [0, 30, 60], [255, 255, 255])
    
    # 形态学去噪
    kernel = np.ones((7, 7), np.uint8)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)
    
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
                    cv2.CHAIN_APPROX_SIMPLE)
    if contours:
        c = max(contours, key=cv2.contourArea)
        hull = cv2.convexHull(c)
        # 提取最高点作为"指尖"
        topmost = hull[hull[:, :, 1].argmin()][0]
        return topmost

self.hands = mp.solutions.hands.Hands(
    model_complexity=0,  # 0=lite, 1=full (默认)
    max_num_hands=1,
    min_detection_confidence=0.5,  # 降低阈值换速度
    min_tracking_confidence=0.5
)

# 移除耗时的关键点绘制
# mp_drawing.draw_landmarks(frame, landmarks, connections)  # 注释掉
drawLandmarks=False  # 新增开关

# 640x480 已经是最优平衡点
# 若进一步降低到320x240可提升FPS,但会影响检测精度
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)  # 减少延迟

# Python部分
# 检测控制文件
if os.path.exists('/tmp/snake_quit'):
    print('Quit command detected')
    break

if os.path.exists('/tmp/snake_restart'):
    os.remove('/tmp/snake_restart')
    # 重置游戏状态
    self.game.gameOver = False
    self.game.points = []
    self.game.previousHead = (0, 0)

fingers = detector.fingersUp()  # 返回5个值,1表示手指伸直

# 模式1: 选择工具(食指+中指伸直)
if fingers[1] and fingers[2]:
    if y1 < 153:  # 在顶部工具栏区域
        if 0 < x1 < 320:    color = [50, 128, 250]  # 蓝色
        elif 320 < x1 < 640:  color = [0, 0, 255]    # 红色
        elif 640 < x1 < 960:  color = [0, 255, 0]    # 绿色
        elif 960 < x1 < 1280: color = [0, 0, 0]     # 橡皮擦

# 模式2: 绘画(仅食指伸直)
elif fingers[1] and not fingers[2]:
    cv2.line(imgCanvas, (xp, yp), (x1, y1), color, brushThickness)

# 1. 将画布转为灰度图并二值化
imgGray = cv2.cvtColor(imgCanvas, cv2.COLOR_BGR2GRAY)
_, imgInv = cv2.threshold(imgGray, 50, 255, cv2.THRESH_BINARY_INV)

# 2. 使用位运算合成
img = cv2.bitwise_and(img, imgInv)    # 摄像头画面保留非绘画区域
img = cv2.bitwise_or(img, imgCanvas)   # 叠加绘画内容

def create_header(self):
    """动态生成工具栏"""
    header = np.zeros((100, self.width, 3), np.uint8)
    header[:] = (200, 200, 200)  # 灰色背景
    
    tools = [
        ((250, 128, 50), "Blue"),  # BGR格式
        ((0, 0, 255), "Red"),
        ((0, 255, 0), "Green"),
        ((0, 0, 0), "Eraser")
    ]
    
    section_width = self.width // 4
    for i, (color, label) in enumerate(tools):
        x1 = i * section_width
        x2 = (i + 1) * section_width
        
        # 绘制颜色块
        cv2.rectangle(header, (x1 + 10, 20), (x2 - 10, 80), color, -1)
        cv2.rectangle(header, (x1 + 10, 20), (x2 - 10, 80), 
               (255, 255, 255), 2)  # 白色边框
        
        # 文字标签
        cv2.putText(header, label, (x1 + 20, 95),
              cv2.FONT_HERSHEY_SIMPLEX, 0.5, (50, 50, 50), 1)
    
    return header

width = 1280, height = 720
画布: imgCanvas = np.zeros((720, 1280, 3), np.uint8)

width = 640, height = 480  # 降低50%分辨率
画布: imgCanvas = np.zeros((480, 640, 3), np.uint8)

section_width = self.width // 4  # 自适应宽度
if y1 < 100:  # 工具栏高度
    if 0 < x1 < section_width:
        self.color = (250, 128, 50)  # 蓝色
    elif section_width < x1 < section_width * 2:
        self.color = (0, 0, 255)   # 红色
# ...

if all(x >= 1 for x in fingers):
    imgCanvas = np.zeros((720, 1280, 3), np.uint8)

# Python端
if os.path.exists('/tmp/painter_clear'):
    os.remove('/tmp/painter_clear')
    self.imgCanvas = np.zeros((self.height, self.width, 3), np.uint8)
    print("Canvas cleared")

def detect_hand(self, frame):
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, [0, 30, 60], [25, 255, 255])  # 肤色范围

    # 形态学处理去噪
    kernel = np.ones((7, 7), np.uint8)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)

    # 查找最大轮廓
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if contours:
        c = max(contours, key=cv2.contourArea)
        if cv2.contourArea(c) > 3000:  # 面积阈值过滤噪声
            return c, mask
    return None, mask

def recognize(self, contour):
    hull = cv2.convexHull(contour, returnPoints=False)
    defects = cv2.convexityDefects(contour, hull)

    finger_count = 0
    for i in range(defects.shape[0]):
        s, e, f, d = defects[i, 0]
        start = tuple(contour[s][0])  # 凸点1
        end = tuple(contour[e][0])    # 凸点2
        far = tuple(contour[f][0])    # 凹点(指间)

        # 计算角度判断是否为有效指尖
        a = np.linalg.norm(np.array(start) - np.array(end))
        b = np.linalg.norm(np.array(start) - np.array(far))
        c = np.linalg.norm(np.array(end) - np.array(far))
        angle = np.arccos((b**2 + c**2 - a**2) / (2 * b * c))
        if angle <= np.pi / 2.2 and d > 8000:  # 角度和深度阈值
            finger_count += 1

    gestures = ["Fist/One", "Two", "Three", "Four", "Five"]
    return gestures[finger_count]

proc = subprocess.Popen(['gst-launch-1.0', 'fdsrc', '!', ...], stdin=subprocess.PIPE)
proc.stdin.write(frame_data)

# Python端
cv2.imwrite(f'/dev/shm/gesture_frames/frame_{frame_index:03d}.jpg', processed)

# Python端: 直接往FIFO写JPEG数据
fifo = open('/tmp/gesture_fifo', 'wb')
_, jpeg = cv2.imencode('.jpg', processed, [cv2.IMWRITE_JPEG_QUALITY, 85])
fifo.write(jpeg.tobytes())
fifo.flush()

#!/usr/bin/env python3
# fixed_display_test.py

import subprocess
import time
import os

def check_camera_devices():
    """检查可用的摄像头设备"""
    print("=== 摄像头设备检查 ===")
    
    try:
        # 使用v4l2-ctl检查设备
        result = subprocess.run(["v4l2-ctl", "--list-devices"],
                        capture_output=True, text=True)
        if result.returncode == 0:
            print("找到的视频设备:")n            print(result.stdout)
        else:
            print("v4l2-ctl命令执行失败")
    except Exception as e:
        print(f"检查摄像头设备失败: {e}")

    # 测试常见的摄像头设备
    camera_devices = ["/dev/video11", "/dev/video0", "/dev/video1", "/dev/video2"]
    print("\n测试摄像头设备:")
    
    for device in camera_devices:
        if os.path.exists(device):
            print(f"测试设备: {device}")
            try:
                # 尝试使用GStreamer测试摄像头
                cmd = [
                  "gst-launch-1.0",
                  "-v",
                  "v4l2src", f"device={device}", "!", 
                  "video/x-raw,width=640,height=480,framerate=15/1", "!",
                  "videoconvert", "!", 
                  "waylandsink", "sync=false"
                ]
                
                process = subprocess.Popen(cmd)
                time.sleep(3) # 显示3秒
                process.terminate()
                process.wait()
                print(f" {device}: 摄像头工作正常")
                return device
            
            except Exception as e:
                print(f"{device}: 测试失败 - {e}")
        else:
            print(f"{device}: 设备不存在")
    
    return None

def test_static_patterns():
    """测试静态图案（不会变化）"""
    print("\n=== 静态图案测试 ===")

    # 设置Wayland环境
    os.environ['WAYLAND_DISPLAY'] = 'wayland-0'

    # 测试静态图案（不会变化）
    static_patterns = [
        ("smpte100", "SMPTE 100%色彩条"),
        ("ball", "时钟图案"),
        ("blink", "闪烁图案"),
        ("pinwheel", "风车图案"),
        ("spokes", "辐条图案"),
    ]

    for pattern, description in static_patterns:
        print(f"显示: {description}")
        try:
            cmd = [
                "gst-launch-1.0",
                "videotestsrc", f"pattern={pattern}", "!",
                "video/x-raw,width=480,height=800,framerate=15/1", "!",
                "waylandsink", "sync=false"
            ]
            
            process = subprocess.Popen(cmd)
            time.sleep(3)
            process.terminate()
            process.wait()
            print(f"{description} 显示成功")
        
        except Exception as e:
            print(f"{description} 显示失败: {e}")

def test_custom_resolution():
    """测试自定义分辨率显示"""
    print("\n=== 自定义分辨率测试 ===")

    resolutions = [
        (480, 800, "竖屏 480x800"),
        (800, 480, "横屏 800x480"),
        (640, 480, "标准 640x480"),
        (400, 800, "竖屏 400x800"),
    ]

    for width, height, desc in resolutions:
        print(f"测试分辨率: {desc}")
        try:
            cmd = [
                "gst-launch-1.0",
                "videotestsrc", "pattern=smpte100", "!",
                f"video/x-raw,width={width},height={height},framerate=15/1", "!",
                "videoconvert", "!",
                "waylandsink", "sync=false"
            ]
            
            process = subprocess.Popen(cmd)
            time.sleep(2)
            process.terminate()
            process.wait()
            print(f" {desc} 显示成功")
        
        except Exception as e:
            print(f" {desc} 显示失败: {e}")

if __name__ == "__main__":
    print("=" * 50)

    # 1. 检查摄像头
    camera_device = check_camera_devices()

    # 2. 测试静态图案
    test_static_patterns()

    # 3. 测试不同分辨率
    test_custom_resolution()

    print("\n" + "=" * 50)
    if camera_device:
        print(f"可用的摄像头设备: {camera_device}")
    else:
        print("未找到可用的摄像头设备")
    print("所有测试完成")