是否可以使用 Google 的 Vision API 或 Amazon 的 Rekognition 来获取对象的数量？

Question

我一直在探索使用 AWS Rekognition 和 Google 的 Vision 来获取图像/视频中对象的数量，但一直找不到出路。尽管在 Google 的 Vision 网站上，他们确实有一个“从图像中洞察”的部分，显然，数量似乎已被捕获。

附件是该 URL 的快照。

有人可以建议是否可以使用 Google 的 Vision 或任何其他 API 来帮助获取图像中的对象数量。谢谢

编辑：

例如 - 对于下图所示的图像，返回的数量应为 10 辆汽车。 正如 Torry Yang 在他的回答中所建议的，标签注释计数可以给出所需的数量，但似乎并非如此，因为标签注释的计数是 18。返回的对象有点像这样。

"labelAnnotations": [
    {
      "mid": "/m/0k4j",
      "description": "car",
      "score": 0.98658943,
      "topicality": 0.98658943
    },
    {
      "mid": "/m/012f08",
      "description": "motor vehicle",
      "score": 0.9631113,
      "topicality": 0.9631113
    },
    {
      "mid": "/m/07yv9",
      "description": "vehicle",
      "score": 0.9223521,
      "topicality": 0.9223521
    },
    {
      "mid": "/m/01w71f",
      "description": "personal luxury car",
      "score": 0.8976857,
      "topicality": 0.8976857
    },
    {
      "mid": "/m/068mqj",
      "description": "automotive design",
      "score": 0.8736646,
      "topicality": 0.8736646
    },
    {
      "mid": "/m/012mq4",
      "description": "sports car",
      "score": 0.8418799,
      "topicality": 0.8418799
    },
    {
      "mid": "/m/01lcwm",
      "description": "luxury vehicle",
      "score": 0.7761523,
      "topicality": 0.7761523
    },
    {
      "mid": "/m/06j11d",
      "description": "performance car",
      "score": 0.76816446,
      "topicality": 0.76816446
    },
    {
      "mid": "/m/03vnt4",
      "description": "mid size car",
      "score": 0.75732976,
      "topicality": 0.75732976
    },
    {
      "mid": "/m/03vntj",
      "description": "full size car",
      "score": 0.6855145,
      "topicality": 0.6855145
    },
    {
      "mid": "/m/0h8ls87",
      "description": "automotive exterior",
      "score": 0.66056395,
      "topicality": 0.66056395
    },
    {
      "mid": "/m/014f__",
      "description": "supercar",
      "score": 0.592226,
      "topicality": 0.592226
    },
    {
      "mid": "/m/02swz_",
      "description": "compact car",
      "score": 0.5807265,
      "topicality": 0.5807265
    },
    {
      "mid": "/m/0h6dlrc",
      "description": "bmw",
      "score": 0.5801241,
      "topicality": 0.5801241
    },
    {
      "mid": "/m/01h80k",
      "description": "muscle car",
      "score": 0.55745816,
      "topicality": 0.55745816
    },
    {
      "mid": "/m/021mp2",
      "description": "sedan",
      "score": 0.5522745,
      "topicality": 0.5522745
    },
    {
      "mid": "/m/0369ss",
      "description": "city car",
      "score": 0.52938646,
      "topicality": 0.52938646
    },
    {
      "mid": "/m/01d1dj",
      "description": "coupé",
      "score": 0.50642073,
      "topicality": 0.50642073
    }
  ]

Answer 1

在 Google Cloud Vision 上，您应该能够获得计数。例如，如果你想用 Python 计算人脸的数量，你可以这样做：

def detect_faces(path):
    """Detects faces in an image."""
    client = vision.ImageAnnotatorClient()

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.types.Image(content=content)

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

注意最后一行。在每种受支持的语言中，您应该能够计算结果。

以下是获取每个标签计数的方法。

def detect_labels(path):
    """Detects labels in the file."""
    client = vision.ImageAnnotatorClient()

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.types.Image(content=content)

    response = client.label_detection(image=image)
    labels = response.label_annotations

    count = {}
    for label in labels:
        if label in count:
            count[label] += 1
        else:
            count[label] = 1

在第二个示例中，计数将是每个标签及其在图像中出现的次数的字典。

Answer 2

Google Vision 和AWS Rekognition 都不支持照片中的对象计数。

https://forums.aws.amazon.com/thread.jspa?threadID=254814

但是，您可以在 Vision 和 Rekognition 中计算图像中的人脸数量。

在 AWS Rekognition 中，您会收到 DetectFaces API 的 json 响应：

HTTP/1.1 200 OK
Content-Type: application/x-amz-json-1.1
Date: Wed, 04 Jan 2017 23:37:03 GMT
x-amzn-RequestId: b1827570-d2d6-11e6-a51e-73b99a9bb0b9
Content-Length: 1355
Connection: keep-alive


{
   "FaceDetails":[
      {
         "BoundingBox":{
            "Height":0.18000000715255737,
            "Left":0.5555555820465088,
            "Top":0.33666667342185974,
            "Width":0.23999999463558197
         },
         "Confidence":100.0,
         "Landmarks":[
            {
               "Type":"eyeLeft",
               "X":0.6394737362861633,
               "Y":0.40819624066352844
            },
            {
               "Type":"eyeRight",
               "X":0.7266660928726196,
               "Y":0.41039225459098816
            },
            {
               "Type":"nose",
               "X":0.6912462115287781,
               "Y":0.44240960478782654
            },
            {
               "Type":"mouthLeft",
               "X":0.6306198239326477,
               "Y":0.46700039505958557
            },
            {
               "Type":"mouthRight",
               "X":0.7215608954429626,
               "Y":0.47114261984825134
            }
         ],
         "Pose":{
            "Pitch":4.050806522369385,
            "Roll":0.9950747489929199,
            "Yaw":13.693790435791016
         },
         "Quality":{
            "Brightness":37.60169982910156,
            "Sharpness":80.0
         }
      },
      {
         "BoundingBox":{
            "Height":0.16555555164813995,
            "Left":0.3096296191215515,
            "Top":0.7066666483879089,
            "Width":0.22074073553085327
         },
         "Confidence":99.99998474121094,
         "Landmarks":[
            {
               "Type":"eyeLeft",
               "X":0.3767718970775604,
               "Y":0.7863991856575012
            },
            {
               "Type":"eyeRight",
               "X":0.4517287313938141,
               "Y":0.7715709209442139
            },
            {
               "Type":"nose",
               "X":0.42001065611839294,
               "Y":0.8192070126533508
            },
            {
               "Type":"mouthLeft",
               "X":0.3915625810623169,
               "Y":0.8374140858650208
            },
            {
               "Type":"mouthRight",
               "X":0.46825936436653137,
               "Y":0.823401689529419
            }
         ],
         "Pose":{
            "Pitch":-16.320178985595703,
            "Roll":-15.097439765930176,
            "Yaw":-5.771541118621826
         },
         "Quality":{
            "Brightness":31.440860748291016,
            "Sharpness":60.000003814697266
         }
      }
   ],
   "OrientationCorrection":"ROTATE_0"
}

然后，您可以使用此响应来计算边界框的数量，这些边界框最终将对应于图像中的人脸数量。

此外，如果您想对照片中的对象进行计数，您可以在 AWS SageMaker 上设置自定义机器学习模型来执行此操作。示例：https://github.com/cosmincatalin/object-counting-with-mxnet-and-sagemaker