I have been exploring to get the count of the objects in an image / video using AWS Rekognition & Google's Vision, but haven't been able to find a way out. Though at [Google's Vision](https://cloud.google.com/vision/) site, they do have a section 'Insight from the Images' where apparently it seems like that the quantity has been captured. 

Attached is a snapshot from that URL.[![Vision](https://i.stack.imgur.com/fR3Ew.png)](https://i.stack.imgur.com/fR3Ew.png) 

Can someone please suggest if it is possible with Google's Vision or any other API which can help in getting the count of objects in an image. Thanks

Edit:

For example - For the image shown below, the count returned should be 10 cars. As Torry Yang suggested in his answer, the label Annotations count can give the required number but it does not seem to be the case as the count for label annotations is 18. The returned object is somewhat like this.

`"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
 }
 ]` 

[![image](https://i.stack.imgur.com/eN8lS.jpg)](https://i.stack.imgur.com/eN8lS.jpg)