python-2.7 - 通过网络摄像头检测

Question

我正在尝试创建一个主要功能，该功能将使用下面的程序和网络摄像头来显示当前在屏幕上检测到的卡。最好的方法是什么？任何帮助表示赞赏。

 """

Usage: 

  ./card_img.py filename, num_cards, training_image_filename, training_labels_filename, num_training_cards

Example:
  ./card_img.py test.JPG 4 train.png train.tsv 56


"""

import sys
import numpy as np
sys.path.insert(0, "/usr/local/lib/python2.7/site-packages/") 
import cv2


###############################################################################
# Utility code
###############################################################################

def rectify(h):
#//changes the array of the picture\\
  h = h.reshape((4,2))
  hnew = np.zeros((4,2),dtype = np.float32)

  add = h.sum(1)
  hnew[0] = h[np.argmin(add)]
  hnew[2] = h[np.argmax(add)]

  diff = np.diff(h,axis = 1)
  hnew[1] = h[np.argmin(diff)]
  hnew[3] = h[np.argmax(diff)]

  return hnew

###############################################################################
# Image Matching  
###############################################################################
def preprocess(img):
#Example: test_img = cv2.imread("test.JPG")
#         preprocess(test_img)
  gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
  blur = cv2.GaussianBlur(gray,(5,5),2 )
  thresh = cv2.adaptiveThreshold(blur,255,1,1,11,1)
  return thresh

def imgdiff(img1,img2):
  img1 = cv2.GaussianBlur(img1,(5,5),5)
  img2 = cv2.GaussianBlur(img2,(5,5),5)    
  diff = cv2.absdiff(img1,img2)  
  diff = cv2.GaussianBlur(diff,(5,5),5)    
  flag, diff = cv2.threshold(diff, 200, 255, cv2.THRESH_BINARY) 
  return np.sum(diff)  

def find_closest_card(training,img):
  features = preprocess(img)
  return sorted(training.values(), key=lambda x:imgdiff(x[1],features))[0][0]


###############################################################################
# Card Extraction
###############################################################################  
def getCards(im, numcards=4):
#Example: test = cv2.imread("test.JPG") //also works with 1 arg(file)\\
#         getCards(test,2)
#or:      getCards("test.jpg",2) 
  gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
  blur = cv2.GaussianBlur(gray,(1,1),1000)
  flag, thresh = cv2.threshold(blur, 120, 255, cv2.THRESH_BINARY) 

  contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

  contours = sorted(contours, key=cv2.contourArea,reverse=True)[:numcards]  

  for card in contours:
    peri = cv2.arcLength(card,True)
    approx = rectify(cv2.approxPolyDP(card,0.02*peri,True))

    # box = np.int0(approx)
    # cv2.drawContours(im,[box],0,(255,255,0),6)
    # imx = cv2.resize(im,(1000,600))
    # cv2.imshow('a',imx)      

    h = np.array([ [0,0],[449,0],[449,449],[0,449] ],np.float32)

    transform = cv2.getPerspectiveTransform(approx,h)
    warp = cv2.warpPerspective(im,transform,(450,450))

    yield warp


def get_training(training_labels_filename,training_image_filename,num_training_cards,avoid_cards=None):
#Example: get_training("train.tsv","train.png",4,None)
  training = {}

  labels = {}
  for line in file(training_labels_filename): 
    key, num, suit = line.strip().split()
    labels[int(key)] = (num,suit)

  print "Training"

  im = cv2.imread(training_image_filename)
  for i,c in enumerate(getCards(im,num_training_cards)):
    if avoid_cards is None or (labels[i][0] not in avoid_cards[0] and labels[i][1] not in avoid_cards[1]):
      training[i] = (labels[i], preprocess(c))

  print "Done training"
  return training


if __name__ == '__main__':
  if len(sys.argv) == 6:
    filename = sys.argv[1]
    num_cards = int(sys.argv[2])
    training_image_filename = sys.argv[3]
    training_labels_filename = sys.argv[4]    
    num_training_cards = int(sys.argv[5])

    training = get_training(training_labels_filename,training_image_filename,num_training_cards)

    im = cv2.imread("test.")

    width = im.shape[0]
    height = im.shape[1]
    if width < height:
      im = cv2.transpose(im)
      im = cv2.flip(im,1)

    # Debug: uncomment to see registered images
    #for i,c in enumerate(getCards(im,num_cards)):
    #   card = find_closest_card(training,c,)
    #   cv2.imshow(str(card),c)
    #   cv2.waitKey(0) 

    cards = [find_closest_card(training,c) for c in getCards(im,num_cards)]
    print cards

  else:
    print __doc__

Answer 1

使用非自由模块构建开放式简历并使用 SURF 功能。它们很棒，几乎可以检测和分离所有东西:

http://docs.opencv.org/master/doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.html

以下是一些代码示例(抱歉，只是 C++ 而不是 python):http://docs.opencv.org/doc/user_guide/ug_features2d.html

这是重要的代码:

// detecting keypoints
SurfFeatureDetector detector(400);
vector<KeyPoint> keypoints1, keypoints2;
detector.detect(img1, keypoints1);
detector.detect(img2, keypoints2);

// computing descriptors
SurfDescriptorExtractor extractor;
Mat descriptors1, descriptors2;
extractor.compute(img1, keypoints1, descriptors1);
extractor.compute(img2, keypoints2, descriptors2);

// matching descriptors
BruteForceMatcher<L2<float> > matcher;
vector<DMatch> matches;
matcher.match(descriptors1, descriptors2, matches);

如果速度很重要，您可以使用
cv::FlannBasedMatcher 匹配器；

std::vector< cv::DMatch > matches;
matcher.match( descriptors_this, descriptors_givenImage, matches );

FLANN 只是一个非常酷、非常快的框架，它使用了很多方法，如树、bvhs 和其他东西来加快速度。

如果您在安装 surf 方面需要帮助，这里有一些答案: OpenCV - undefined reference: SurfFeatureDetector and BruteForceMatcher