Python cv2 模块，DescriptorExtractor_create() 实例源码

def im_detect_and_describe(img, mask=None, detector='dense', descriptor='SIFT', colorspace='gray',
                           step=4, levels=7, scale=np.sqrt(2)): 
    """ 
    Describe image using dense sampling / specific detector-descriptor combination. 
    """
    detector = get_detector(detector=detector, step=step, levels=levels, scale=scale)
    extractor = cv2.DescriptorExtractor_create(descriptor)

    try:     
        kpts = detector.detect(img, mask=mask)
        kpts, desc = extractor.compute(img, kpts)

        if descriptor == 'SIFT': 
            kpts, desc = root_sift(kpts, desc)

        pts = np.vstack([kp.pt for kp in kpts]).astype(np.int32)
        return pts, desc

    except Exception as e: 
        print 'im_detect_and_describe', e
        return None, None

def obtainSimilarityScore(img1,img2):
    detector = cv2.FeatureDetector_create("SIFT")
    descriptor = cv2.DescriptorExtractor_create("SIFT")
    skp = detector.detect(img1)
    skp, sd = descriptor.compute(img1, skp)
    tkp = detector.detect(img2)
    tkp, td = descriptor.compute(img2, tkp)
    num1 = 0
    for i in range(len(sd)):
          kp_value_min = np.inf
          kp_value_2min = np.inf
          for j in range(len(td)):
               kp_value = 0
               for k in range(128):
                     kp_value = (sd[i][k]-td[j][k]) *(sd[i][k]-td[j][k]) + kp_value
               if kp_value < kp_value_min:
                     kp_value_2min = kp_value_min
                     kp_value_min = kp_value
          if kp_value_min < 0.8*kp_value_2min:
               num1 = num1+1     
    num2 = 0
    for i in range(len(td)):
          kp_value_min = np.inf
          kp_value_2min = np.inf
          for j in range(len(sd)):
               kp_value = 0
               for k in range(128):
                     kp_value = (td[i][k]-sd[j][k]) *(td[i][k]-sd[j][k]) + kp_value
               if kp_value < kp_value_min:
                     kp_value_2min = kp_value_min
                     kp_value_min = kp_value
          if kp_value_min < 0.8*kp_value_2min:
               num2 = num2+1
    K1 = num1*1.0/len(skp)
    K2 = num2*1.0/len(tkp)
    SimilarityScore  = 100*(K1+K2)*1.0/2    
    return SimilarityScore

def calculate_feature(bin_data):
    """
    calculate the feature data of an image

    parameter :
        'bin_data' is the binary stream format of an image
    return value :
        a tuple of ( keypoints, descriptors, (height,width) )
        keypoints is like [ pt1, pt2, pt3, ... ]
        descriptors is a numpy array
    """
    buff=numpy.frombuffer(bin_data,numpy.uint8)
    img_obj=cv2.imdecode(buff,cv2.CV_LOAD_IMAGE_GRAYSCALE)
    surf=cv2.FeatureDetector_create("SURF")
    surf.setInt("hessianThreshold",400)
    surf_extractor=cv2.DescriptorExtractor_create("SURF")
    keypoints=surf.detect(img_obj,None)
    keypoints,descriptors=surf_extractor.compute(img_obj,keypoints)
    res_keypoints=[]
    for point in keypoints:
        res_keypoints.append(point.pt)
    del buff
    del surf
    del surf_extractor
    del keypoints
    return res_keypoints,numpy.array(descriptors),img_obj.shape

def __init__(self, detector_name, feat_type):
        self.feat_type = feat_type        
        self.detector = cv2.FeatureDetector_create(detector_name)
        self.descriptor_ex = cv2.DescriptorExtractor_create(feat_type)

def main(image_file):
    image = Image.open(image_file)
    if image is None:
        print 'Could not load image "%s"' % sys.argv[1]
        return

    image = np.array(image.convert('RGB'), dtype=np.uint8)
    image = image[:, :, ::-1].copy()

    winSize = (200, 200)
    stepSize = 32

    roi = extractRoi(image, winSize, stepSize)
    weight_map, mask_scale = next(roi)

    samples = [(rect, scale, cv2.cvtColor(window, cv2.COLOR_BGR2GRAY))
               for rect, scale, window in roi]

    X_test = [window for rect, scale, window in samples]
    coords = [(rect, scale) for rect, scale, window in samples]

    extractor = cv2.FeatureDetector_create('SURF')
    detector = cv2.DescriptorExtractor_create('SURF')

    affine = AffineInvariant(extractor, detector)

    saved = pickle.load(open('classifier.pkl', 'rb'))

    feature_transform = saved['pipe']
    model = saved['model']

    print 'Extracting Affine transform invariant features'
    affine_invariant_features = affine.transform(X_test)
    print 'Matching features with template'
    features = feature_transform.transform(affine_invariant_features)

    rects = classify(model, features, coords, weight_map, mask_scale)
    for (left, top, right, bottom) in non_max_suppression_fast(rects, 0.4):
        cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 0), 10)
        cv2.rectangle(image, (left, top), (right, bottom), (32, 32, 255), 5)

    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    plt.show()