树莓派利用OpenCV的图像跟踪、人脸识别等

import cv2a=cv2.imread("/home/pi/2020-06-15-162551_1920x1080_scrot.png")cv2.imshow("test",a)cv2.waitKey()cv2.destroyAllWindows()

import cv2cap = cv2.VideoCapture(0)while True:    ret, frame = cap.read()    cv2.imshow('frame', frame)    # 这一步必须有，否则图像无法显示    if cv2.waitKey(1) & 0xFF == ord('q'):        break
#当一切完成时，释放捕获cap.release()cv2.destroyAllWindows()

import sysimport cv2import mathimport timeimport threadingimport numpy as npimport HiwonderSDK.yaml_handle as yaml_handle
if sys.version_info.major == 2:    print('Please run this program with python3!')    sys.exit(0)
range_rgb = {    'red':   (0, 0, 255),    'blue':  (255, 0, 0),    'green': (0, 255, 0),    'black': (0, 0, 0),    'white': (255, 255, 255)}
__target_color = ('red', 'green', 'blue')lab_data = yaml_handle.get_yaml_data(yaml_handle.lab_file_path)    # 找出面积最大的轮廓# 参数为要比较的轮廓的列表def getAreaMaxContour(contours):        contour_area_temp = 0    contour_area_max = 0    area_max_contour = None
    for c in contours:  # 历遍所有轮廓        contour_area_temp = math.fabs(cv2.contourArea(c))  # 计算轮廓面积        if contour_area_temp > contour_area_max:            contour_area_max = contour_area_temp            if contour_area_temp > 300:  # 只有在面积大于300时，最大面积的轮廓才是有效的，以过滤干扰                area_max_contour = c
    return area_max_contour, contour_area_max  # 返回最大的轮廓
detect_color = Nonecolor_list = []start_pick_up = Falsesize = (640, 480)def run(img):    global rect    global detect_color    global start_pick_up    global color_list            img_copy = img.copy()    frame_resize = cv2.resize(img_copy, size, interpolation=cv2.INTER_NEAREST)    frame_gb = cv2.GaussianBlur(frame_resize, (3, 3), 3)    frame_lab = cv2.cvtColor(frame_gb, cv2.COLOR_BGR2LAB)  # 将图像转换到LAB空间    color_area_max = None    max_area = 0    areaMaxContour_max = 0    if not start_pick_up:        for i in lab_data:            if i in __target_color:                frame_mask = cv2.inRange(frame_lab,                                             (lab_data[i]['min'][0],                                              lab_data[i]['min'][1],                                              lab_data[i]['min'][2]),                                             (lab_data[i]['max'][0],                                              lab_data[i]['max'][1],                                              lab_data[i]['max'][2]))  #对原图像和掩模进行位运算                opened = cv2.morphologyEx(frame_mask, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))  # 开运算                closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))  # 闭运算                contours = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # 找出轮廓                areaMaxContour, area_max = getAreaMaxContour(contours)  # 找出最大轮廓                                if areaMaxContour is not None:                    if area_max > max_area:  # 找最大面积                        max_area = area_max                        color_area_max = i                        areaMaxContour_max = areaMaxContour                if max_area > 500:  # 有找到最大面积            rect = cv2.minAreaRect(areaMaxContour_max)            box = np.int0(cv2.boxPoints(rect))            y = int((box[1][0]-box[0][0])/2+box[0][0])            x = int((box[2][1]-box[0][1])/2+box[0][1])            print('X:',x,'Y:',y) #打印坐标            cv2.drawContours(img, [box], -1, range_rgb[color_area_max], 2)            if not start_pick_up:                if color_area_max == 'red':  # 红色最大                    color = 1                elif color_area_max == 'green':  # 绿色最大                    color = 2                elif color_area_max == 'blue':  # 蓝色最大                    color = 3                else:                    color = 0                color_list.append(color)                if len(color_list) == 3:  # 多次判断                    # 取平均值                    color = int(round(np.mean(np.array(color_list))))                    color_list = []                    if color == 1:                        detect_color = 'red'
                    elif color == 2:                        detect_color = 'green'
                    elif color == 3:                        detect_color = 'blue'
                    else:                        detect_color = 'None'
##    cv2.putText(img, "Color: " + detect_color, (10, img.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65, detect_color, 2)    return img
if __name__ == '__main__':        cap = cv2.VideoCapture(-1) #读取摄像头    __target_color = ('red',)    while True:        ret, img = cap.read()        if ret:            frame = img.copy()            Frame = run(frame)                       cv2.imshow('Frame', Frame)            key = cv2.waitKey(1)            if key == 27:                break        else:            time.sleep(0.01)    cv2.destroyAllWindows()

import osimport sysimport cv2import mathimport timeimport numpy as npimport HiwonderSDK.Misc as Misc
if sys.version_info.major == 2:    print('Please run this program with python3!')    sys.exit(0)
__finger = 0__t1 = 0__step = 0__count = 0__get_finger = False
# 初始位置def initMove():    pass
def reset():    global __finger, __t1, __step, __count, __get_finger    __finger = 0    __t1 = 0    __step = 0    __count = 0    __get_finger = False    def init():    reset()    initMove()
class Point(object):  # 一个坐标点    x = 0    y = 0
    def __init__(self, x=0, y=0):        self.x = x        self.y = y
class Line(object):  # 一条线    def __init__(self, p1, p2):        self.p1 = p1        self.p2 = p2
def GetCrossAngle(l1, l2):    '''    求两直线之间的夹角    :param l1:    :param l2:    :return:    '''    arr_0 = np.array([(l1.p2.x - l1.p1.x), (l1.p2.y - l1.p1.y)])    arr_1 = np.array([(l2.p2.x - l2.p1.x), (l2.p2.y - l2.p1.y)])    cos_value = (float(arr_0.dot(arr_1)) / (np.sqrt(arr_0.dot(arr_0))                                            * np.sqrt(arr_1.dot(arr_1))))   # 注意转成浮点数运算    return np.arccos(cos_value) * (180/np.pi)
def distance(start, end):    """    计算两点的距离    :param start: 开始点    :param end: 结束点    :return: 返回两点之间的距离    """    s_x, s_y = start    e_x, e_y = end    x = s_x - e_x    y = s_y - e_y    return math.sqrt((x**2)+(y**2))
def image_process(image, rw, rh):  # hsv    '''    # 光线影响，请修改 cb的范围    # 正常黄种人的Cr分量大约在140~160之间    识别肤色    :param image: 图像    :return: 识别后的二值图像    '''    frame_resize = cv2.resize(image, (rw, rh), interpolation=cv2.INTER_CUBIC)    YUV = cv2.cvtColor(frame_resize, cv2.COLOR_BGR2YCR_CB)  # 将图片转化为YCrCb    _, Cr, _ = cv2.split(YUV)  # 分割YCrCb    Cr = cv2.GaussianBlur(Cr, (5, 5), 0)        _, Cr = cv2.threshold(Cr, 135, 160, cv2.THRESH_BINARY +                          cv2.THRESH_OTSU)  # OTSU 二值化
    # 开运算，去除噪点    open_element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))    opend = cv2.morphologyEx(Cr, cv2.MORPH_OPEN, open_element)    # 腐蚀    kernel = np.ones((3, 3), np.uint8)    erosion = cv2.erode(opend, kernel, iterations=3)
    return erosion
def get_defects_far(defects, contours, img):    '''    获取凸包中最远的点    '''    if defects is None and contours is None:        return None    far_list = []    for i in range(defects.shape[0]):        s, e, f, d = defects[i, 0]        start = tuple(contours[s][0])        end = tuple(contours[e][0])        far = tuple(contours[f][0])        # 求两点之间的距离        a = distance(start, end)        b = distance(start, far)        c = distance(end, far)        # 求出手指之间的角度        angle = math.acos((b ** 2 + c ** 2 - a ** 2) /                          (2 * b * c)) * 180 / math.pi        # 手指之间的角度一般不会大于100度        # 小于90度        if angle <= 75:  # 90:            # cv.circle(img, far, 10, [0, 0, 255], 1)            far_list.append(far)    return far_list
def get_max_coutour(cou, max_area):    '''    找出最大的轮廓    根据面积来计算，找到最大后，判断是否小于最小面积，如果小于侧放弃    :param cou: 轮廓    :return: 返回最大轮廓    '''    max_coutours = 0    r_c = None    if len(cou) < 1:        return None    else:        for c in cou:            # 计算面积            temp_coutours = math.fabs(cv2.contourArea(c))            if temp_coutours > max_coutours:                max_coutours = temp_coutours                cc = c        # 判断所有轮廓中最大的面积        if max_coutours > max_area:            r_c = cc        return r_c
def find_contours(binary, max_area):    '''    CV_RETR_EXTERNAL - 只提取最外层的轮廓    CV_RETR_LIST - 提取所有轮廓，并且放置在 list 中    CV_RETR_CCOMP - 提取所有轮廓，并且将其组织为两层的 hierarchy: 顶层为连通域的外围边界，次层为洞的内层边界。    CV_RETR_TREE - 提取所有轮廓，并且重构嵌套轮廓的全部 hierarchy    method  逼近方法 (对所有节点, 不包括使用内部逼近的 CV_RETR_RUNS).    CV_CHAIN_CODE - Freeman 链码的输出轮廓. 其它方法输出多边形(定点序列).    CV_CHAIN_APPROX_NONE - 将所有点由链码形式翻译(转化）为点序列形式    CV_CHAIN_APPROX_SIMPLE - 压缩水平、垂直和对角分割，即函数只保留末端的象素点;    CV_CHAIN_APPROX_TC89_L1,    CV_CHAIN_APPROX_TC89_KCOS - 应用 Teh-Chin 链逼近算法. CV_LINK_RUNS - 通过连接为 1 的水平碎片使用完全不同的轮廓提取算法    :param binary: 传入的二值图像    :return: 返回最大轮廓    '''    # 找出所有轮廓    contours = cv2.findContours(        binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]    # 返回最大轮廓    return get_max_coutour(contours, max_area)
def get_hand_number(binary_image, contours, rw, rh, rgb_image):    '''    :param binary_image:    :param rgb_image:    :return:    '''    # # 2、找出手指尖的位置    # # 查找轮廓，返回最大轮廓    x = 0    y = 0    coord_list = []    new_hand_list = []  # 获取最终的手指间坐标
    if contours is not None:        # 周长  0.035 根据识别情况修改，识别越好，越小        epsilon = 0.020 * cv2.arcLength(contours, True)        # 轮廓相似        approx = cv2.approxPolyDP(contours, epsilon, True)        # cv2.approxPolyDP()的参数2(epsilon)是一个距离值，表示多边形的轮廓接近实际轮廓的程度，值越小，越精确；参数3表示是否闭合        # cv2.polylines(rgb_image, [approx], True, (0, 255, 0), 1)#画多边形
        if approx.shape[0] >= 3:  # 有三个点以上#多边形最小为三角形，三角形需要三个点            approx_list = []            for j in range(approx.shape[0]):  # 将多边形所有的点储存在一个列表里                # cv2.circle(rgb_image, (approx[j][0][0],approx[j][0][1]), 5, [255, 0, 0], -1)                approx_list.append(approx[j][0])            approx_list.append(approx[0][0])    # 在末尾添加第一个点            approx_list.append(approx[1][0])    # 在末尾添加第二个点
            for i in range(1, len(approx_list) - 1):                p1 = Point(approx_list[i - 1][0],                           approx_list[i - 1][1])    # 声明一个点                p2 = Point(approx_list[i][0], approx_list[i][1])                p3 = Point(approx_list[i + 1][0], approx_list[i + 1][1])                line1 = Line(p1, p2)    # 声明一条直线                line2 = Line(p2, p3)                angle = GetCrossAngle(line1, line2)     # 获取两条直线的夹角                angle = 180 - angle     #                # print angle                if angle < 42:  # 求出两线相交的角度，并小于37度的                    #cv2.circle(rgb_image, tuple(approx_list[i]), 5, [255, 0, 0], -1)                    coord_list.append(tuple(approx_list[i]))
        ##############################################################################        # 去除手指间的点        # 1、获取凸包缺陷点，最远点点        #cv2.drawContours(rgb_image, contours, -1, (255, 0, 0), 1)        try:            hull = cv2.convexHull(contours, returnPoints=False)            # 找凸包缺陷点 。返回的数据， 【起点，终点， 最远的点， 到最远点的近似距离】            defects = cv2.convexityDefects(contours, hull)            # 返回手指间的点            hand_coord = get_defects_far(defects, contours, rgb_image)        except:            return rgb_image, 0                # 2、从coord_list 去除最远点        alike_flag = False        if len(coord_list) > 0:            for l in range(len(coord_list)):                for k in range(len(hand_coord)):                    if (-10 <= coord_list[l][0] - hand_coord[k][0] <= 10 and                            -10 <= coord_list[l][1] - hand_coord[k][1] <= 10):    # 最比较X,Y轴, 相近的去除                        alike_flag = True                        break   #                if alike_flag is False:                    new_hand_list.append(coord_list[l])                alike_flag = False            # 获取指尖的坐标列表并显示            for i in new_hand_list:                j = list(tuple(i))                j[0] = int(Misc.map(j[0], 0, rw, 0, 640))                j[1] = int(Misc.map(j[1], 0, rh, 0, 480))                cv2.circle(rgb_image, (j[0], j[1]), 20, [0, 255, 255], -1)    fingers = len(new_hand_list)
    return rgb_image, fingers

def run(img, debug=False):
    global __act_map, __get_finger    global __step, __count, __finger
    binary = image_process(img, 320, 240)    contours = find_contours(binary, 3000)    img, finger = get_hand_number(binary, contours, 320, 240, img)    if not __get_finger:        if finger == __finger:            __count += 1        else:            __count = 0        __finger = finger            cv2.putText(img, "Finger(s):%d" % __finger, (50, 480 - 30),                cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 255), 2)#将识别到的手指个数写在图片上        return img 
if __name__ == '__main__':        init()    cap = cv2.VideoCapture(-1) #读取摄像头    while True:        ret, img = cap.read()        if ret:            frame = img.copy()            Frame = run(frame)                       frame_resize = cv2.resize(Frame, (320, 240))            cv2.imshow('frame', frame_resize)            key = cv2.waitKey(1)            if key == 27:                break        else:            time.sleep(0.01)    cv2.destroyAllWindows()

import sysimport cv2import mathimport timeimport threadingimport numpy as npimport HiwonderSDK.tm1640 as tmimport RPi.GPIO as GPIO
GPIO.setwarnings(False)GPIO.setmode(GPIO.BCM)
color_range = {'red': [(0, 101, 177), (255, 255, 255)], 'green': [(47, 0, 135), (255, 119, 255)], 'blue': [(0, 0, 0), (255, 255, 115)], 'black': [(0, 0, 0), (41, 255, 136)], 'white': [(193, 0, 0), (255, 250, 255)], }
if sys.version_info.major == 2:    print('Please run this program with python3!')    sys.exit(0)    range_rgb = {    'red': (0, 0, 255),    'blue': (255, 0, 0),    'green': (0, 255, 0),    'black': (0, 0, 0),    'white': (255, 255, 255),}
# 找出面积最大的轮廓# 参数为要比较的轮廓的列表def getAreaMaxContour(contours):    contour_area_temp = 0    contour_area_max = 0    area_max_contour = None
    for c in contours:  # 历遍所有轮廓        contour_area_temp = math.fabs(cv2.contourArea(c))  # 计算轮廓面积        if contour_area_temp > contour_area_max:            contour_area_max = contour_area_temp            if contour_area_temp > 50:  # 只有在面积大于50时，最大面积的轮廓才是有效的，以过滤干扰                area_max_contour = c
    return area_max_contour, contour_area_max  # 返回最大的轮廓
shape_length = 0
def move():    global shape_length        while True:        if shape_length == 3:            print('三角形')            ## 显示'三角形'            tm.display_buf = (0x80, 0xc0, 0xa0, 0x90, 0x88, 0x84, 0x82, 0x81,                              0x81, 0x82, 0x84,0x88, 0x90, 0xa0, 0xc0, 0x80)            tm.update_display()                    elif shape_length == 4:            print('矩形')            ## 显示'矩形'            tm.display_buf = (0x00, 0x00, 0x00, 0x00, 0xff, 0x81, 0x81, 0x81,                              0x81, 0x81, 0x81,0xff, 0x00, 0x00, 0x00, 0x00)            tm.update_display()                    elif shape_length >= 6:                       print('圆')            ## 显示'圆形'            tm.display_buf = (0x00, 0x00, 0x00, 0x00, 0x1c, 0x22, 0x41, 0x41,                              0x41, 0x22, 0x1c,0x00, 0x00, 0x00, 0x00, 0x00)            tm.update_display()                    time.sleep(0.01)               # 运行子线程th = threading.Thread(target=move)th.setDaemon(True)th.start()
shape_list = []action_finish = True
if __name__ == '__main__':        cap = cv2.VideoCapture(-1)    while True:        ret,img = cap.read()        if ret:            img_copy = img.copy()            img_h, img_w = img.shape[:2]            frame_gb = cv2.GaussianBlur(img_copy, (3, 3), 3)                  frame_lab = cv2.cvtColor(frame_gb, cv2.COLOR_BGR2LAB)  # 将图像转换到LAB空间            max_area = 0            color_area_max = None                areaMaxContour_max = 0
            if action_finish:                for i in color_range:                    if i != 'white':                        frame_mask = cv2.inRange(frame_lab, color_range[i][0], color_range[i][1])  #对原图像和掩模进行位运算                        opened = cv2.morphologyEx(frame_mask, cv2.MORPH_OPEN, np.ones((6,6),np.uint8))  #开运算                        closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, np.ones((6,6),np.uint8)) #闭运算                        contours = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  #找出轮廓                        areaMaxContour, area_max = getAreaMaxContour(contours)  #找出最大轮廓                        if areaMaxContour is not None:                            if area_max > max_area:#找最大面积                                max_area = area_max                                color_area_max = i                                areaMaxContour_max = areaMaxContour            if max_area > 200:                                   cv2.drawContours(img, areaMaxContour_max, -1, (0, 0, 255), 2)                # 识别形状                # 周长  0.035 根据识别情况修改，识别越好，越小                epsilon = 0.035 * cv2.arcLength(areaMaxContour_max, True)                # 轮廓相似                approx = cv2.approxPolyDP(areaMaxContour_max, epsilon, True)                shape_list.append(len(approx))                if len(shape_list) == 30:                    shape_length = int(round(np.mean(shape_list)))                                                shape_list = []                    print(shape_length)                                frame_resize = cv2.resize(img, (320, 240))            cv2.imshow('frame', frame_resize)            key = cv2.waitKey(1)            if key == 27:                break        else:            time.sleep(0.01)    my_camera.camera_close()    cv2.destroyAllWindows()

import cv2import sysfrom pyzbar import pyzbar
if sys.version_info.major == 2:    print('Please run this program with python3!')    sys.exit(0)
def run(image):    # 找到图像中的条形码并解码每个条形码    barcodes = pyzbar.decode(image)    # 循环检测到的条形码    for barcode in barcodes:        # 提取条形码的边界框位置        (x, y, w, h) = barcode.rect        # 绘出图像上条形码的边框        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
        barcodeData = barcode.data.decode("utf-8")        barcodeType = barcode.type        # 在图像上绘制条形码数据和条形码类型        text = "{} ({})".format(barcodeData, barcodeType)        cv2.putText(image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    return image
if __name__ == '__main__':        cap = cv2.VideoCapture(-1) #读取摄像头        while True:        ret, img = cap.read()        if ret:            frame = img.copy()            Frame = run(frame)                       cv2.imshow('Frame', Frame)            key = cv2.waitKey(1)            if key == 27:                break        else:            time.sleep(0.01)    cv2.destroyAllWindows()

cd /home/pi/Downloadssudo pip3 install apriltag-0.0.16-cp37-cp37m-linux_armv7l.whl

import sysimport cv2import mathimport timeimport threadingimport numpy as npimport apriltag
#apriltag检测
if sys.version_info.major == 2:    print('Please run this program with python3!')    sys.exit(0)
object_center_x = 0.0object_center_y = 0.0
# 检测apriltagdetector = apriltag.Detector(searchpath=apriltag._get_demo_searchpath())def apriltagDetect(img):    global object_center_x, object_center_y        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    detections = detector.detect(gray, return_image=False)
    if len(detections) != 0:        for detection in detections:                                   corners = np.rint(detection.corners)  # 获取四个角点            cv2.drawContours(img, [np.array(corners, np.int)], -1, (0, 255, 255), 2)
            tag_family = str(detection.tag_family, encoding='utf-8')  # 获取tag_family            tag_id = int(detection.tag_id)  # 获取tag_id
            object_center_x, object_center_y = int(detection.center[0]), int(detection.center[1])  # 中心点                        object_angle = int(math.degrees(math.atan2(corners[0][1] - corners[1][1], corners[0][0] - corners[1][0])))  # 计算旋转角                        return tag_family, tag_id                return None, None
def run(img):    global state    global tag_id    global action_finish    global object_center_x, object_center_y         img_h, img_w = img.shape[:2]        tag_family, tag_id = apriltagDetect(img) # apriltag检测        if tag_id is not None:        print('X:',object_center_x,'Y:',object_center_y)        cv2.putText(img, "tag_id: " + str(tag_id), (10, img.shape[0] - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.65, [0, 255, 255], 2)        cv2.putText(img, "tag_family: " + tag_family, (10, img.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65, [0, 255, 255], 2)    else:        cv2.putText(img, "tag_id: None", (10, img.shape[0] - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.65, [0, 255, 255], 2)        cv2.putText(img, "tag_family: None", (10, img.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65, [0, 255, 255], 2)        return img
if __name__ == '__main__':        cap = cv2.VideoCapture(-1) #读取摄像头        while True:        ret, img = cap.read()        if ret:            frame = img.copy()            Frame = run(frame)                       cv2.imshow('Frame', Frame)            key = cv2.waitKey(1)            if key == 27:                break        else:            time.sleep(0.01)    cv2.destroyAllWindows()