python开启摄像头以及深度学习实现目标检测方法
最近想做实时目标检测，需要用到python开启摄像头，我手上只有两个uvc免驱的摄像头，性能一般。利用python开启摄像头费了一番功夫，主要原因是我的摄像头都不能用cv2的VideCapture打开，这让我联想到原来opencv也打不开Android手机上的摄像头（后来采用QML的Camera模块实现的）。看来opencv对于摄像头的兼容性仍然不是很完善。
我尝了几种办法：v4l2，v4l2_capture以及simpleCV，都打不开。最后采用pygame实现了摄像头的采集功能，这里直接给大家分享具体实现代码（python3.6，cv2，opencv3.3，ubuntu16.04）。中间注释的部分是我上述方法打开摄像头的尝试，说不定有适合自己的。
importpygame.camera
importtime
importpygame
importcv2
importnumpyasnp
defsurface_to_string(surface):
"""convertpygamesurfaceintostring"""
returnpygame.image.tostring(surface,'RGB')
defpygame_to_cvimage(surface):
"""converpygamesurfaceintocvimage"""
#cv_image=np.zeros(surface.get_size,np.uint8,3)
image_string=surface_to_string(surface)
image_np=np.fromstring(image_string,np.uint8).reshape(480,640,3)
frame=cv2.cvtColor(image_np,cv2.COLOR_BGR2RGB)
returnimage_np,frame
pygame.camera.init()
pygame.camera.list_cameras()
cam=pygame.camera.Camera("/dev/video0",[640,480])
cam.start()
time.sleep(0.1)
screen=pygame.display.set_mode([640,480])
whileTrue:
image=cam.get_image()
cv_image,frame=pygame_to_cvimage(image)
screen.fill([0,0,0])
screen.blit(image,(0,0))
pygame.display.update()
cv2.imshow('frame',frame)
key=cv2.waitKey(1)
ifkey&0xFF==ord('q'):
break
#pygame.image.save(image,"pygame1.jpg")
cam.stop()
上述代码需要注意一个地方，就是pygame图片和opencv图片的转化（pygame_to_cvimage）有些地方采用cv.CreateImageHeader和SetData来实现，注意这两个函数在opencv3+后就消失了。因此采用numpy进行实现。
至于目标检测，由于现在网上有很多实现的方法，MobileNet等等。这里我不讲解具体原理，因为我的研究方向不是这个，这里直接把代码贴出来，亲测成功了。
fromimutils.videoimportFPS
importargparse
importimutils
importv4l2
importfcntl
importv4l2capture
importselect
importimage
importpygame.camera
importpygame
importcv2
importnumpyasnp
importtime
defsurface_to_string(surface):
"""convertpygamesurfaceintostring"""
returnpygame.image.tostring(surface,'RGB')
defpygame_to_cvimage(surface):
"""converpygamesurfaceintocvimage"""
#cv_image=np.zeros(surface.get_size,np.uint8,3)
image_string=surface_to_string(surface)
image_np=np.fromstring(image_string,np.uint8).reshape(480,640,3)
frame=cv2.cvtColor(image_np,cv2.COLOR_BGR2RGB)
returnframe
ap=argparse.ArgumentParser()
ap.add_argument("-p","--prototxt",required=True,help="pathtocaffedeployprototxtfile")
ap.add_argument("-m","--model",required=True,help="pathtocaffepretrainedmodel")
ap.add_argument("-c","--confidence",type=float,default=0.2,help="minimumprobabilitytofilterweakdetection")
args=vars(ap.parse_args())
CLASSES=["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow",
"diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
COLORS=np.random.uniform(0,255,size=(len(CLASSES),3))
print("[INFO]loadingmodel...")
net=cv2.dnn.readNetFromCaffe(args["prototxt"],args["model"])
print("[INFO]startingvideostream...")
######opencv########
#vs=VideoStream(src=1).start()
#
#camera=cv2.VideoCapture(0)
#ifnotcamera.isOpened():
#print("cameraisnotopen")
#time.sleep(2.0)
######v4l2########
#vd=open('/dev/video0','r')
#cp=v4l2.v4l2_capability()
#fcntl.ioctl(vd,v4l2.VIDIOC_QUERYCAP,cp)
#cp.driver
#####v4l2_capture
#video=v4l2capture.Video_device("/dev/video0")
#size_x,size_y=video.set_format(640,480,fourcc='MJPEG')
#video.create_buffers(30)
#video.queue_all_buffers()
#video.start()
#####pygame####
pygame.camera.init()
pygame.camera.list_cameras()
cam=pygame.camera.Camera("/dev/video0",[640,480])
cam.start()
time.sleep(1)
fps=FPS().start()
whileTrue:
#try:
#frame=vs.read()
#except:
#print("cameraisnotopened")
#frame=imutils.resize(frame,width=400)
#(h,w)=frame.shape[:2]
#grabbed,frame=camera.read()
#ifnotgrabbed:
#break
#select.select((video,),(),())
#frame=video.read_and_queue()
#npfs=np.frombuffer(frame,dtype=np.uint8)
#print(len(npfs))
#frame=cv2.imdecode(npfs,cv2.IMREAD_COLOR)
image=cam.get_image()
frame=pygame_to_cvimage(image)
frame=imutils.resize(frame,width=640)
blob=cv2.dnn.blobFromImage(frame,0.00783,(640,480),127.5)
net.setInput(blob)
detections=net.forward()
foriinnp.arange(0,detections.shape[2]):
confidence=detections[0,0,i,2]
ifconfidence>args["confidence"]:
idx=int(detections[0,0,i,1])
box=detections[0,0,i,3:7]*np.array([640,480,640,480])
(startX,startY,endX,endY)=box.astype("int")
label="{}:{:.2f}%".format(CLASSES[idx],confidence*100)
cv2.rectangle(frame,(startX,startY),(endX,endY),COLORS[idx],2)
y=startY-15ifstartY-15>15elsestartY+15
cv2.putText(frame,label,(startX,y),cv2.FONT_HERSHEY_SIMPLEX,0.5,COLORS[idx],2)
cv2.imshow("Frame",frame)
key=cv2.waitKey(1)&0xFF
ifkey==ord("q"):
break
fps.stop()
print("[INFO]elapsedtime:{:.2f}".format(fps.elapsed()))
print("[INFO]approx.FPS:{:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
#vs.stop()
上面的实现需要用到两个文件，是caffe实现好的模型，我直接上传（文件名为MobileNetSSD_deploy.caffemodel和MobileNetSSD_deploy.prototxt，上google能够下载到）。
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