detect_features.py~

#from opensfm import dataset
from opensfm import features
from opensfm import context
import numpy as np
import time
import logging
import argparse
import os.path,sys
import cv2
import matplotlib.pyplot as plt

def parse_para():
  
  path = os.path.join(args.dataset,'images')
  if os.path.isfile(os.path.join(args.dataset,'imageslist.txt')):
    os.remove(os.path.join(args.dataset,'imageslist.txt'))
  for name in os.listdir(path):
    if is_image_file(name):
      with open(os.path.join(args.dataset,'imageslist.txt'), 'a') as fout:
        fout.write(args.dataset + '/images/' + name+'\n')

def read_image_list(L=None):
  if os.path.isfile(os.path.join(args.dataset,'imageslist.txt')):
    with open(os.path.join(args.dataset,'imageslist.txt')) as fin:
      for line in fin:
        #L.append(line)
        L.append(line.strip('\n'))
    return True
  else:
    parse_para()
    return False

def is_image_file(name):
  return name.split('.')[-1].lower() in {'jpg', 'jpeg', 'png', 'tif'}

def detect_feature(L):
  print (cv2.__version__)
  IMREAD_COLOR = cv2.IMREAD_COLOR if context.OPENCV3 else cv2.CV_LOAD_IMAGE_COLOR
  image = cv2.imread(L[1], IMREAD_COLOR)[:,:,::-1]  # Turn BGR to RGB
  #image = cv2.imread(L[0],1)[:,:,::-1]
  #gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
  #print image_single
  plt.imshow(image, cmap='gray')
  plt.show()
  detector = cv2.FeatureDetector_create('SIFT')
  descriptor = cv2.DescriptorExtractor_create('SIFT')
  #detector.setDouble("contrastThreshold", 0.1)
  #detector.setDouble('edgeThreshold', 10)
  points = detector.detect(image)

if __name__ == '__main__':
  logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
  start=time.time()
  parse=argparse.ArgumentParser(description="Detect image features")
  parse.add_argument('dataset',help='add a images folder')
  args=parse.parse_args()  
  parse_para()
  L=list()
  read_image_list(L)
  print(L)
  detect_feature(L)