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树莓派(Raspberry Pi)是各种派中最早开始流行起来的一个嵌入式Linux板卡,其本质是尺寸仅有信用卡大小的一个小型电脑。你可以将树莓派连接电视、显示器、键盘鼠标等设备当一台正常Linux电脑使用,当然,现在也已经支持windows了。

作者丨woshigaowei5146


 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

(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_ide



准备


  • 树莓派4B
  • USB免驱摄像头


配置


安装python-opencv,

安装numpy,pip3 install-U numpy

安装opencv-python,opencv-contrib-python,


测试


图片:

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

视频:

import cv2
cap = 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 sys
import cv2
import math
import time
import threading
import numpy as np
import 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 = None
color_list = []
start_pick_up = False
size = (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:

(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_树莓派_02


人脸识别






利用了Caffe训练的人脸数据集。

import sys
import numpy as np
import cv2
import math
import time
import 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 = True
x1=x2=y1=y2 = 0
old_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()

(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_python_03


手势识别






import os
import sys
import cv2
import math
import time
import numpy as np
import 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()


(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_树莓派_04


形状识别






import sys
import cv2
import math
import time
import threading
import numpy as np
import HiwonderSDK.tm1640 as tm
import 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。

(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_ide_05


条码识别






首先安装pyzbar,pip3 install pyzbar

import cv2
import sys
from 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()


(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_树莓派_06


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/Downloads
sudo pip3 install apriltag-0.0.16-cp37-cp37m-linux_armv7l.whl


(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_ide_07


import sys
import cv2
import math
import time
import threading
import numpy as np
import apriltag

#apriltag检测

if sys.version_info.major == 2:
print('Please run this program with python3!')
sys.exit(0)

object_center_x = 0.0
object_center_y = 0.0

# 检测apriltag
detector = 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()


(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_python_08



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-python
sudo apt-get install opencv-contrib-python

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

sudo apt-get install opencv-python
sudo apt-get install opencv-contrib-python

pip install opencv-contrib-python
pip 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 rpi
deb-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 main
deb-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/simple
pip 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/Downloads
sudo pip3 install opencv_python-3.4.10.37-cp37-cp37m-linux_armv7l.whl


ImportError:numpy.core.multiarray failed to import






先卸载低版本的numpy,再安装新版本的numpy,即

pip uninstall numpy
pip install -U numpy


无效。

pip install numpy --upgrade --force


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/


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:



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。

(附代码)树莓派利用OpenCV的图像跟踪、人脸识别等_python_09

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