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树莓派利用OpenCV的图像跟踪、人脸识别等

新机器视觉 | 34 2022-07-17 08:51 0 0 0
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作者丨woshigaowei5146 @CSDN

编辑丨3D视觉开发者社区

 content


准备

配置

测试

程序

      颜色识别跟踪

      人脸识别

      手势识别

      形状识别

      条码识别

      二维码识别

故障问题解决

     module 'cv2' has no attribute 'dnn'
     ImportError:numpy.core.multiarray failed to import             1121:error:(-2:Unspecified error)FAILED:fs.is_open(). Can't open


准备

  • 树莓派4B

  • USB免驱摄像头

配置

安装python-opencv,参考:https://blog.csdn.net/weixin_45911959/article/details/122709090

安装numpy,pip3 install-U numpy

安装opencv-python,opencv-contrib-python,参考:https://blog.csdn.net/weixin_57605235/article/details/121512923


测试

图片:

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

视频:

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()


程序

颜色识别跟踪

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()


效果:


人脸识别

利用了Caffe训练的人脸数据集。

import sysimport numpy as npimport cv2import mathimport timeimport threading
# 人脸检测if sys.version_info.major == 2: print('Please run this program with python3!') sys.exit(0)
# 阈值conf_threshold = 0.6
# 模型位置modelFile = "/home/pi/mu_code/models/res10_300x300_ssd_iter_140000_fp16.caffemodel"configFile = "/home/pi/mu_code/models/deploy.prototxt"net = cv2.dnn.readNetFromCaffe(configFile, modelFile)
frame_pass = Truex1=x2=y1=y2 = 0old_time = 0
def run(img): global old_time global frame_pass global x1,x2,y1,y2
if not frame_pass: frame_pass = True cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2, 8) x1=x2=y1=y2 = 0 return img else: frame_pass = False
img_copy = img.copy() img_h, img_w = img.shape[:2] blob = cv2.dnn.blobFromImage(img_copy, 1, (100, 100), [104, 117, 123], False, False) net.setInput(blob) detections = net.forward() #计算识别
for i in range(detections.shape[2]): confidence = detections[0, 0, i, 2] if confidence > conf_threshold: #识别到的人了的各个坐标转换会未缩放前的坐标 x1 = int(detections[0, 0, i, 3] * img_w) y1 = int(detections[0, 0, i, 4] * img_h) x2 = int(detections[0, 0, i, 5] * img_w) y2 = int(detections[0, 0, i, 6] * img_h) cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2, 8) #将识别到的人脸框出 X = (x1 + x2)/2 Y = (y1 + y2)/2 print('X:',X,'Y:',Y) 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(


手势识别

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()



形状识别

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()


approxPolyDP()函数用于将一个连续光滑曲线折线化。

以代码"approx=cv2.approxPolyDP(areaMaxContour_max,epsilon,True)”为例,括号内的参数含义如下:

第一个参数“areaMaxContour_max”是输入的形状轮廓;

第二个参数“epsilon”是距离值,表示多边形的轮廓接近实际轮廓的程度,值越小,越精确;

第三个参数“True”表示轮廓为闭合曲线。


cv2.approxPolyDP()函数的输出为近似多边形的顶点坐标,根据顶点的数量判断形状。


条码识别

首先安装pyzbar,pip3 install pyzbar

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()



二维码识别

安装apriltag,发现安装失败。还是老办法下载到本地以后安装。

在https://www.piwheels.org/simple/apriltag/,我下载了apriltag-0.0.16-cp37-cp37mlinux_armv7l.whl。

使用FileZilla传输到树莓派,打开whl文件所在的树莓派目录,安装whl文件,显示成功安装。

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()



故障问题解决

module ‘cv2’ has no attribute ‘dnn’

尝试用一下指令都有问题,一直在报错,或者显示无法识别 python-opencv,更换镜像也没用:

sudo apt install python-opencv 或 sudo apt install python3-opencv 
sudo apt-get install opencv-pythonsudo apt-get install opencv-contrib-python
pip install opencv-contrib-pythonpip install opencv-python

最后,通过下载本地文件的方式安装成功。
首先习惯更新树莓派系统和文件

sudo apt-get update sudo apt-get upgrade 

若下载速度太慢可以考虑换源。

1) 使用“ sudo nano /etc/apt/sources.list” 命令编辑 sources.list 文件,注释原文件所有内容,并追加以下内容:deb http://mirrors.aliyun.com/raspbian/raspbian/ buster main contrib non-free rpideb-src http://mirrors.aliyun.com/raspbian/raspbian/ buster main contrib non-free rpi
使用 Ctrl+O 快捷键保存文件,Ctrl+X 退出文件。
2)使用 “sudo nano /etc/apt/sources.list.d/raspi.list” 命令编辑 raspi.list 文件,注释原文件所有内容,并追加以下内容:deb http://mirrors.tuna.tsinghua.edu.cn/raspbian/raspbian/ buster maindeb-src http://mirrors.tuna.tsinghua.edu.cn/raspbian/raspbian/ buster main
使用 Ctrl+O 快捷键保存文件,Ctrl+X 退出文件。
3)执行“sudo apt-get update” 命令。
4) 为加速 Python pip 安装速度,特更改 Python 软件源,操作方法:打开树莓派命令行,输入下面命令:pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simplepip install pip -U
5) 最后输入指令“sudo reboot”,重新启动树莓派即可。

下载whl文件并传到树莓派上,在电脑上打开 https://www.piwheels.org/simple/opencv-python/


下载与自己python版本相对的whl文件,我下载的是opencv_python-3.4.10.37-cp37-cp37m-linux_armv7l.whl


cp37表示python的版本,armv7表示处理器的架构,树莓派4B选择armv7


将其使用FileZilla传输到树莓派,打开whl文件所在的树莓派目录,安装whl文件,显示成功安装opencv-python

cd /home/pi/Downloadssudo pip3 install opencv_python-3.4.10.37-cp37-cp37m-linux_armv7l.whl

参考:https://blog.csdn.net/weixin_57605235/article/details/121512923

ImportError:numpy.core.multiarray failed to import

先卸载低版本的numpy,再安装新版本的numpy,即

1.  pip uninstall numpy2.  pip install -U numpy

来自https://blog.csdn.net/qq_25603827/article/details/107824977

无效。

pip install numpy --upgrade --force

来自http://www.manongjc.com/article/38668.html

无效。

查看本地numpy版本:

pip show numpy

而我们在安装opencv-python时,其对应numpy版本为:

所以对numpy进行版本降级处理即可:

 pip install -U numpy==1.14.5 -i https://pypi.mirrors.ustc.edu.cn/simple/

来自https://zhuanlan.zhihu.com/p/280702247

无效。

最后,用pip3 install-Unumpy成功。所以用python3的最好还是用pip3。
网上有很多尝试方法,有升级版本的,有降级版本的,各种诡异的现象层出不穷,说法不一,参考:

https://blog.csdn.net/Robin_Pi/article/details/120544691 https://zhuanlan.zhihu.com/p/29026597

1121:error:(-2:Unspecified error) FAILED: fs.is_open(). Can’t open

找了半天发现多了个点在开头。


本文仅做学术分享,如有侵权,请联系删文。


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