使用vision api函数创建数据帧？

Question

我正在使用GoogleAPI函数提取表达式，它将检测图像中的所有人脸

def detect_faces_uri(uri):
    """Detects faces in the file located in Google Cloud Storage or the web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.types.Image()
    image.source.image_uri = uri

    response = client.face_detection(image=image)
    faces = response.face_annotations

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Faces:')

    for face in faces:
        print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
        print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
        print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in face.bounding_poly.vertices])

        print('face bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))

这是我得到的输出：

Faces:
anger: VERY_UNLIKELY
joy: VERY_LIKELY
surprise: VERY_UNLIKELY
face bounds: (1077,157),(2146,157),(2146,1399),(1077,1399)
anger: VERY_UNLIKELY
joy: VERY_UNLIKELY
surprise: VERY_UNLIKELY
face bounds: (144,1273),(793,1273),(793,1844),(144,1844)
anger: VERY_UNLIKELY
joy: VERY_UNLIKELY
surprise: VERY_UNLIKELY
face bounds: (785,167),(1100,167),(1100,534),(785,534)

我需要使用这个功能的几个图像，并希望得到一个数据帧，但我真的不知道我如何才能转换成数据帧输出的方式，我想。。。我需要这样的输出：

所需输出：

URL                   Face      Anger     Joy       Surprised
abc.com               Face1     Likely    Unlikely   Unlikely
abc.com               Face2     Unlikely  Likely    Unlikely
.

。 .

有什么帮助吗

Answer 1

首先启动一个新的空数据帧：

df = pd.DataFrame() 

然后在“打印”命令旁边添加新行：

newline= pd.DataFrame({"x":[vertex.x], "y":[vertex.y]}) 

然后将新行附加到df：

df = df.append(newline)