python開啟攝像頭以及深度學習實現(xiàn)目標檢測方法

更新時間：2018年08月03日 09:15:05 作者：紅色未來

今天小編就為大家分享一篇python開啟攝像頭以及深度學習實現(xiàn)目標檢測方法，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

最近想做實時目標檢測，需要用到python開啟攝像頭，我手上只有兩個uvc免驅的攝像頭，性能一般。利用python開啟攝像頭費了一番功夫，主要原因是我的攝像頭都不能用cv2的VideCapture打開，這讓我聯(lián)想到原來opencv也打不開Android手機上的攝像頭（后來采用QML的Camera模塊實現(xiàn)的）。看來opencv對于攝像頭的兼容性仍然不是很完善。

我嘗了幾種辦法：v4l2，v4l2_capture以及simpleCV，都打不開。最后采用pygame實現(xiàn)了攝像頭的采集功能，這里直接給大家分享具體實現(xiàn)代碼（python3.6，cv2，opencv3.3，ubuntu16.04）。中間注釋的部分是我上述方法打開攝像頭的嘗試，說不定有適合自己的。

import pygame.camera
import time
import pygame
import cv2
import numpy as np
 
def surface_to_string(surface):
 """convert pygame surface into string"""
 return pygame.image.tostring(surface, 'RGB')
 
def pygame_to_cvimage(surface):
 """conver pygame surface into cvimage"""
 
 #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)
 return image_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])
 
while True:
 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)
 if key & 0xFF == ord('q'):
  break
 
 
 #pygame.image.save(image, "pygame1.jpg")
 
cam.stop()

上述代碼需要注意一個地方，就是pygame圖片和opencv圖片的轉化（pygame_to_cvimage）有些地方采用cv.CreateImageHeader和SetData來實現(xiàn)，注意這兩個函數(shù)在opencv3+后就消失了。因此采用numpy進行實現(xiàn)。

至于目標檢測，由于現(xiàn)在網上有很多實現(xiàn)的方法，MobileNet等等。這里我不講解具體原理，因為我的研究方向不是這個，這里直接把代碼貼出來，親測成功了。

from imutils.video import FPS
import argparse
import imutils
 
 
import v4l2
import fcntl
 
import v4l2capture
import select
import image
 
import pygame.camera
import pygame
import cv2
import numpy as np
import time
 
def surface_to_string(surface):
 """convert pygame surface into string"""
 return pygame.image.tostring(surface, 'RGB')
 
def pygame_to_cvimage(surface):
 """conver pygame surface into cvimage"""
 
 #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)
 return frame
 
 
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True, help="path to caffe deploy prototxt file")
ap.add_argument("-m", "--model", required=True, help="path to caffe pretrained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2, help="minimum probability to filter weak detection")
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] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
 
 
print("[INFO] starting video stream ...")
 
###### opencv ########
#vs = VideoStream(src=1).start()
#
#camera = cv2.VideoCapture(0)
#if not camera.isOpened():
# print("camera is not open")
#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()
 
 
while True:
 #try:
 # frame = vs.read()
 #except:
 # print("camera is not opened")
 
 #frame = imutils.resize(frame, width=400)
 #(h, w) = frame.shape[:2]
 
 
 #grabbed, frame = camera.read()
 #if not grabbed:
 # 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()
 
 for i in np.arange(0, detections.shape[2]):
 
  confidence = detections[0, 0, i, 2]
 
  if confidence > 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 - 15 if startY - 15 > 15 else startY + 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
 
 if key ==ord("q"):
  break
 
 
fps.stop()
print("[INFO] elapsed time :{:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS :{:.2f}".format(fps.fps()))
 
 
 
cv2.destroyAllWindows()
 
#vs.stop()

上面的實現(xiàn)需要用到兩個文件，是caffe實現(xiàn)好的模型，我直接上傳（文件名為MobileNetSSD_deploy.caffemodel和MobileNetSSD_deploy.prototxt，上google能夠下載到）。

以上這篇python開啟攝像頭以及深度學習實現(xiàn)目標檢測方法就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

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