Question

我有这个问题。我在flask api上运行此代码

# face verification with the VGGFace2 model
from matplotlib import pyplot
from PIL import Image
from numpy import asarray
from scipy.spatial.distance import cosine
from mtcnn.mtcnn import MTCNN
from keras_vggface.vggface import VGGFace
from keras_vggface.utils import preprocess_input

# extract a single face from a given photograph
def extract_face(filename, required_size=(254, 254)):
    # load image from file
    pixels = pyplot.imread(filename)

    # create the detector, using default weights
    detector = MTCNN()
    # detect faces in the image
    results = detector.detect_faces(pixels)
    # extract the bounding box from the first face
    x1, y1, width, height = results[0]['box']
    x2, y2 = x1 + width, y1 + height
    # extract the face
    face = pixels[y1:y2, x1:x2]
    # resize pixels to the model size
    image = Image.fromarray(face)
    image = image.resize(required_size)
    face_array = asarray(image)
    # print(face_array)
    return face_array

# extract faces and calculate face embeddings for a list of photo files
def get_embeddings(filenames):
    # extract faces
    faces = [extract_face(f) for f in filenames]
    # convert into an array of samples
    samples = asarray(faces, 'float32')
    # prepare the face for the model, e.g. center pixels
    samples = preprocess_input(samples, version=2)
    # create a vggface model
    model = VGGFace(model='vgg16', include_top=False, input_shape=(254, 254, 3), pooling='max')
    # perform prediction
    yhat = model.predict(samples)
    return yhat

# determine if a candidate face is a match for a known face
def is_match(known_embedding, candidate_embedding, thresh=0.45):
    # calculate distance between embeddings
    score = cosine(known_embedding, candidate_embedding)

    print('Match percentage (%.3f)' % (100 - (100 * score)))

    print('>face is a Match (%.3f <= %.3f)' % (score, thresh))

# define filenames
filenames = ['audacious.jpg', 'face-20190717050545949130_123.jpg']
# get embeddings file filenames
embeddings = get_embeddings(filenames)
# define sharon stone
sharon_id = embeddings[0]
# verify known photos of sharon
print('Positive Tests')
is_match(embeddings[0], embeddings[1])

我第一次命中测试，该过程运行良好。但是当第二次命中时会出错：

'numpy.ndarray'对象不可调用

'无法将feed_dict键解释为张量：Tensor Tensor（“ Placeholder：0”，shape =（3，3，3，64），dty   pe = float32）不是该图的元素。'

如果我不在API上运行，则仅在文件中运行，然后使用python3 file.py运行，每次运行时都不会出现任何错误有任何线索吗？

Answer 1

检查此行：

samples = asarray(faces, 'float32')

并尝试将其替换为：

samples = asarray(faces, dtype=np.float32)

如何解决：internal_error：'numpy.ndarray'对象不可调用？

1 个答案: