修改Adafruit_PCA9685目录下的PCA9685.py文件：
sudo find / -name Adafruit_PCA9685 得到 /usr/local/lib/python3.6/dist-packages/Adafruit_PCA9685
cd /usr/local/lib/python3.6/dist-packages/Adafruit_PCA9685
ls
vim PCA9685.py
把 self._device = i2c.get_i2c_device(address, **kwargs) 改为 self._device = i2c.Device(address, "0", None, **kwargs)
然后根据jetbot源码改编得到以下代码
此时就可以使用下面代码控制云台pwm舵机了
import time import Adafruit_PCA9685 pwm = Adafruit_PCA9685.PCA9685() pwm.set_pwm_freq(50) def servo_move(basechannel,basevalue,tiltchannel,tiltvalue): basevalue=4096*((basevalue*11)+500)/20000 pwm.set_pwm(basechannel,0,int(basevalue)) tiltvalue=4096*((tiltvalue*11)+500)/20000 pwm.set_pwm(tiltchannel,0,int(tiltvalue)) pan = 90 tilt = 90 servo_move(0, pan, 1, tilt) if __name__ == '__main__': for angle in range(30, 120, 5): servo_move(0, angle, 1, angle) time.sleep(0.5) servo_move(0, pan, 1, tilt)
应用：
import cv2 #导入库 import numpy as np import time import os from servo_control import * #关于色域范围可以百度 HSV #百科：https://baike.baidu.com/item/HSV/547122?fr=aladdin #参考：https://blog.csdn.net/leo_888/article/details/88284251 # 启动脚本杀死影响程序的进程 os.system('./killmjpg.sh') time.sleep(0.1) # 要识别的颜色字典 color_dist = { 'red': {'Lower': np.array([156, 60, 60]), 'Upper': np.array([180, 255, 255])}, 'green': {'Lower': np.array([35, 43, 35]), 'Upper': np.array([90, 255, 255])}, 'blue': {'Lower': np.array([110, 80, 80]), 'Upper': np.array([128, 255, 255])}, # 'gray': {'Lower': np.array([0,0,30]),'Upper':np.array([255,40,80]) } }; flag = -1 flag_bak = -1 img_w = 320 img_h = 240 cap = cv2.VideoCapture(0) #打开摄像头 最大范围 640×480 cap.set(3,img_w) #设置画面宽度 cap.set(4,img_h) #设置画面高度 lastTime = time.time() time.sleep(1) while 1: #进入无线循环 global pan,tilt #将摄像头拍摄到的画面作为frame的值 ret,frame = cap.read() for i in color_dist: #1-高斯滤波GaussianBlur() 让图片模糊 frame = cv2.GaussianBlur(frame,(5,5),0) #2-转换HSV的样式 以便检测 hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) #3-查找字典 mask = cv2.inRange(hsv, color_dist[i]['Lower'], color_dist[i]['Upper']) #4-腐蚀图像 mask = cv2.erode(mask,None,iterations=2) #高斯模糊 mask = cv2.GaussianBlur(mask,(3,3),0) #图像合并 res = cv2.bitwise_and(frame,frame,mask=mask) #6-边缘检测 cnts = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2] if len(cnts) >0 : #通过边缘检测来确定所识别物体的位置信息得到相对坐标 cnt = max(cnts,key=cv2.contourArea) rect = cv2.minAreaRect(cnt) # 获取最小外接矩形的4个顶点 box = cv2.boxPoints(rect) #获取坐标 长宽 角度 c_x, c_y = rect[0] c_h, c_w = rect[1] c_angle = rect[2] #print(c_x,c_y,c_h,c_w) #判断是否是方块大小且在方框中 if 25 < c_h < 85 and 25 < c_w < 85: # and 50 < c_x < 270 and 60 < c_y < 180: #print('---',c_x,c_y,c_h,c_w) #绘制轮廓 cv2.drawContours(frame, [np.int0(box)], -1, (0, 255, 255), 2) #print(time.time(), 'x=', int(c_x), 'y=', int(c_y), 'c_h=', int(c_h), 'c_w=', int(c_w), 'angle=', int(c_angle)) errorPan = c_x - img_w/2 errorTilt = c_y - img_h/2 #print('errorpan',errorPan) if abs(errorPan)>15: pan=pan-errorPan/75 if abs(errorTilt)>15: tilt=tilt-errorTilt/50 #print('pan',pan) if pan > 180: pan = 180 print("Pan out of Range") if pan < 0: pan = 0 print("pan Out of Range") if tilt > 180: tilt = 180 print("Pan out of Range") if tilt < 0: tilt = 0 print("pan Out of Range") #servo_move(0,180-pan,1,tilt) servo_move(0,pan,1,180-tilt) else: pass cv2.imshow('frame',frame) #将具体的测试效果显示出来 #cv2.imshow('mask',mask) #cv2.imshow('res',res) if cv2.waitKey(5) & 0xFF == 27: #如果按了ESC就退出 当然也可以自己设置 break cap.release() cv2.destroyAllWindows() #后面两句是常规操作,每次使用摄像头都需要这样设置一波
import cv2 #导入库 import numpy as np import time import os from servo_control import * #创建显示控件 from jetcam.utils import bgr8_to_jpeg import traitlets import ipywidgets.widgets as widgets from IPython.display import display disp = widgets.Image(format='jpeg', width=320, height=240) display(disp) #关于色域范围可以百度 HSV #百科：https://baike.baidu.com/item/HSV/547122?fr=aladdin #参考：https://blog.csdn.net/leo_888/article/details/88284251 # 启动脚本杀死影响程序的进程 os.system('./killmjpg.sh') time.sleep(0.1) # 要识别的颜色字典 color_dist = { 'red': {'Lower': np.array([156, 60, 60]), 'Upper': np.array([180, 255, 255])}, 'green': {'Lower': np.array([35, 43, 35]), 'Upper': np.array([90, 255, 255])}, 'blue': {'Lower': np.array([110, 80, 80]), 'Upper': np.array([128, 255, 255])}, # 'gray': {'Lower': np.array([0,0,30]),'Upper':np.array([255,40,80]) } }; flag = -1 flag_bak = -1 img_w = 320 img_h = 240 cap = cv2.VideoCapture(0) #打开摄像头 最大范围 640×480 cap.set(3,img_w) #设置画面宽度 cap.set(4,img_h) #设置画面高度 lastTime = time.time() time.sleep(1) try: while 1: #进入无线循环 global pan,tilt #将摄像头拍摄到的画面作为frame的值 ret,frame = cap.read() for i in color_dist: #1-高斯滤波GaussianBlur() 让图片模糊 frame = cv2.GaussianBlur(frame,(5,5),0) #2-转换HSV的样式 以便检测 hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) #3-查找字典 mask = cv2.inRange(hsv, color_dist[i]['Lower'], color_dist[i]['Upper']) #4-腐蚀图像 mask = cv2.erode(mask,None,iterations=2) #高斯模糊 mask = cv2.GaussianBlur(mask,(3,3),0) #图像合并 res = cv2.bitwise_and(frame,frame,mask=mask) #6-边缘检测 cnts = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2] if len(cnts) >0 : #通过边缘检测来确定所识别物体的位置信息得到相对坐标 cnt = max(cnts,key=cv2.contourArea) rect = cv2.minAreaRect(cnt) # 获取最小外接矩形的4个顶点 box = cv2.boxPoints(rect) #获取坐标 长宽 角度 c_x, c_y = rect[0] c_h, c_w = rect[1] c_angle = rect[2] #print(c_x,c_y,c_h,c_w) #判断是否是方块大小且在方框中 if 25 < c_h < 85 and 25 < c_w < 85: # and 50 < c_x < 270 and 60 < c_y < 180: #print('---',c_x,c_y,c_h,c_w) #绘制轮廓 cv2.drawContours(frame, [np.int0(box)], -1, (0, 255, 255), 2) #print(time.time(), 'x=', int(c_x), 'y=', int(c_y), 'c_h=', int(c_h), 'c_w=', int(c_w), 'angle=', int(c_angle)) errorPan = c_x - img_w/2 errorTilt = c_y - img_h/2 #print('errorpan',errorPan) if abs(errorPan)>15: pan=pan-errorPan/75 if abs(errorTilt)>15: tilt=tilt-errorTilt/50 #print('pan',pan) if pan > 180: pan = 180 print("Pan out of Range") if pan < 0: pan = 0 print("pan Out of Range") if tilt > 180: tilt = 180 print("Pan out of Range") if tilt < 0: tilt = 0 print("pan Out of Range") #servo_move(0,180-pan,1,tilt) servo_move(0,pan,1,180-tilt) else: pass disp.value = bgr8_to_jpeg(frame) #cv2.imshow('frame',frame) #将具体的测试效果显示出来 #cv2.imshow('mask',mask) #cv2.imshow('res',res) #if cv2.waitKey(5) & 0xFF == 27: #如果按了ESC就退出 当然也可以自己设置 #break except KeyboardInterrupt: cap.release() #cv2.destroyAllWindows() #后面两句是常规操作,每次使用摄像头都需要这样设置一波
# OpenCV 摄像头云台人脸追踪 import os import cv2 import numpy as np #import Adafruit_PCA9685 import time from servo_control import * # 启动脚本杀死影响程序的进程 os.system('./killmjpg.sh') time.sleep(0.1) servo_move(0,90,1,90) cam = cv2.VideoCapture(0) dis_w = 320 dis_h = 240 cam.set(3, dis_w) cam.set(4, dis_h) # 载入人脸和眼睛的HAAR 模型 face_cascade = cv2.CascadeClassifier('./trains/haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier('./trains/haarcascade_eye.xml') def Video_display(): global pan, tilt while True: ret,frame = cam.read() gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray,1.3,5) for(x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2) Xcent = x + w/2 Ycent = y + h/2 errorPan = Xcent - dis_w/2 errorTilt = Ycent - dis_h/2 if abs(errorPan)>15: pan=pan-errorPan/50 if abs(errorTilt)>15: tilt=tilt-errorTilt/50 if pan > 180: pan = 180 print("Pan out of Range") if pan < 0: pan = 0 print("pan Out of Range") if tilt > 180: tilt = 180 print("Pan out of Range") if tilt < 0: tilt = 0 print("pan Out of Range") servo_move(0,pan,1,180-tilt) #servo_move(0,pan,1,180-tilt) roi_gray = gray[y:y+h, x:x+w] roi_color = frame[y:y+h, x:x+w] eyes = eye_cascade.detectMultiScale(roi_gray) for (ex,ey,ew,eh) in eyes: cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2) cv2.imshow('frame', frame) if cv2.waitKey(5) & 0xFF == 27: break if __name__ == '__main__': try: Video_display() except KeyboardInterrupt: cap.release() cv2.destroyAllWindows()
# OpenCV 摄像头云台人脸追踪 import os import cv2 import numpy as np #import Adafruit_PCA9685 import time from servo_control import * #创建显示控件 from jetcam.utils import bgr8_to_jpeg import traitlets import ipywidgets.widgets as widgets from IPython.display import display disp = widgets.Image(format='jpeg', width=320, height=240) display(disp) # 启动脚本杀死影响程序的进程 os.system('./killmjpg.sh') time.sleep(0.1) servo_move(0,90,1,90) cam = cv2.VideoCapture(0) dis_w = 320 dis_h = 240 cam.set(3, dis_w) cam.set(4, dis_h) # 载入人脸和眼睛的HAAR 模型 face_cascade = cv2.CascadeClassifier('./trains/haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier('./trains/haarcascade_eye.xml') def Video_display(): global pan, tilt while True: ret,frame = cam.read() gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray,1.3,5) for(x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2) Xcent = x + w/2 Ycent = y + h/2 errorPan = Xcent - dis_w/2 errorTilt = Ycent - dis_h/2 if abs(errorPan)>15: pan=pan-errorPan/50 if abs(errorTilt)>15: tilt=tilt-errorTilt/50 if pan > 180: pan = 180 print("Pan out of Range") if pan < 0: pan = 0 print("pan Out of Range") if tilt > 180: tilt = 180 print("Pan out of Range") if tilt < 0: tilt = 0 print("pan Out of Range") servo_move(0,pan,1,180-tilt) #servo_move(0,pan,1,180-tilt) roi_gray = gray[y:y+h, x:x+w] roi_color = frame[y:y+h, x:x+w] eyes = eye_cascade.detectMultiScale(roi_gray) for (ex,ey,ew,eh) in eyes: cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2) disp.value = bgr8_to_jpeg(frame) #cv2.imshow('frame', frame) #if cv2.waitKey(5) & 0xFF == 27: #break if __name__ == '__main__': try: Video_display() except KeyboardInterrupt: cam.release()
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