13 KiB
YOLO Inference API (UNDER CONSTRUCTION)
The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
API URL
The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
https://api.ultralytics.com/inference/v1
To access the API with a specific model and your API key, you can include them as query parameters in the API URL. The model parameter refers to the MODEL_ID you want to use for inference, and the key parameter corresponds to your API_KEY.
The complete API URL with the model and API key parameters would be:
https://api.ultralytics.com/inference/v1?model=MODEL_ID&key=API_KEY
Replace MODEL_ID with the ID of the model you want to use and API_KEY with your actual API key from https://hub.ultralytics.com/settings?tab=api+keys.
Example Usage in Python
To access the YOLO Inference API with the specified model and API key using Python, you can use the following code:
import requests
api_key = "API_KEY"
model_id = "MODEL_ID"
url = f"https://api.ultralytics.com/inference/v1?model={model_id}&key={api_key}"
image_path = "image.jpg"
with open(image_path, "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, files=files)
print(response.text)
In this example, replace API_KEY with your actual API key, MODEL_ID with the desired model ID, and image.jpg with the path to the image you want to analyze.
Example Usage with CLI
You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the curl command. Replace API_KEY with your actual API key, MODEL_ID with the desired model ID, and image.jpg with the path to the image you want to analyze:
curl -X POST -F image=@image.jpg "https://api.ultralytics.com/inference/v1?model=MODEL_ID&key=API_KEY"
Passing Arguments
This command sends a POST request to the YOLO Inference API with the specified model and key parameters in the URL, along with the image file specified by @image.jpg.
Here's an example of passing the model, key, and normalize arguments via the API URL using the requests library in Python:
import requests
api_key = "API_KEY"
model_id = "MODEL_ID"
url = "https://api.ultralytics.com/inference/v1"
# Define your query parameters
params = {
"key": api_key,
"model": model_id,
"normalize": "True"
}
image_path = "image.jpg"
with open(image_path, "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, files=files, params=params)
print(response.text)
In this example, the params dictionary contains the query parameters key, model, and normalize, which tells the API to return all values in normalized image coordinates from 0 to 1. The normalize parameter is set to "True" as a string since query parameters should be passed as strings. These query parameters are then passed to the requests.post() function.
This will send the query parameters along with the file in the POST request. Make sure to consult the API documentation for the list of available arguments and their expected values.
Return JSON format
The YOLO Inference API returns a JSON list with the detection results. The format of the JSON list will be the same as the one produced locally by the results[0].tojson() command.
The JSON list contains information about the detected objects, their coordinates, classes, and confidence scores.
Detect Model Format
YOLO detection models, such as yolov8n.pt, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
!!! example "Detect Model JSON Response"
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n.pt')
# Run inference
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "CLI API"
```commandline
curl -X POST -F image=@image.jpg https://api.ultralytics.com/inference/v1?model=MODEL_ID,key=API_KEY
```
=== "Python API"
```python
import requests
api_key = "API_KEY"
model_id = "MODEL_ID"
url = "https://api.ultralytics.com/inference/v1"
# Define your query parameters
params = {
"key": api_key,
"model": model_id,
}
image_path = "image.jpg"
with open(image_path, "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, files=files, params=params)
print(response.text)
```
=== "JSON Response"
```json
[
{
"name": "person",
"class": 0,
"confidence": 0.8359682559967041,
"box": {
"x1": 0.08974208831787109,
"y1": 0.27418340047200523,
"x2": 0.8706787109375,
"y2": 0.9887352837456598
}
},
{
"name": "person",
"class": 0,
"confidence": 0.8189555406570435,
"box": {
"x1": 0.5847355842590332,
"y1": 0.05813225640190972,
"x2": 0.8930277824401855,
"y2": 0.9903111775716146
}
},
{
"name": "tie",
"class": 27,
"confidence": 0.2909725308418274,
"box": {
"x1": 0.3433395862579346,
"y1": 0.6070465511745877,
"x2": 0.40964522361755373,
"y2": 0.9849439832899306
}
}
]
```
Segment Model Format
YOLO segmentation models, such as yolov8n-seg.pt, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
!!! example "Segment Model JSON Response"
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n-seg.pt')
# Run inference
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "CLI API"
```commandline
curl -X POST -F image=@image.jpg https://api.ultralytics.com/inference/v1?model=MODEL_ID,key=API_KEY
```
=== "Python API"
```python
import requests
api_key = "API_KEY"
model_id = "MODEL_ID"
url = "https://api.ultralytics.com/inference/v1"
# Define your query parameters
params = {
"key": api_key,
"model": model_id,
}
image_path = "image.jpg"
with open(image_path, "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, files=files, params=params)
print(response.text)
```
=== "JSON Response"
Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
```json
[
{
"name": "person",
"class": 0,
"confidence": 0.856913149356842,
"box": {
"x1": 0.1064866065979004,
"y1": 0.2798851860894097,
"x2": 0.8738358497619629,
"y2": 0.9894873725043403
},
"segments": {
"x": [
0.421875,
0.4203124940395355,
0.41718751192092896
...
],
"y": [
0.2888889014720917,
0.2916666567325592,
0.2916666567325592
...
]
}
},
{
"name": "person",
"class": 0,
"confidence": 0.8512625694274902,
"box": {
"x1": 0.5757311820983887,
"y1": 0.053943040635850696,
"x2": 0.8960096359252929,
"y2": 0.985154045952691
},
"segments": {
"x": [
0.7515624761581421,
0.75,
0.7437499761581421
...
],
"y": [
0.0555555559694767,
0.05833333358168602,
0.05833333358168602
...
]
}
},
{
"name": "tie",
"class": 27,
"confidence": 0.6485961675643921,
"box": {
"x1": 0.33911995887756347,
"y1": 0.6057066175672743,
"x2": 0.4081430912017822,
"y2": 0.9916408962673611
},
"segments": {
"x": [
0.37187498807907104,
0.37031251192092896,
0.3687500059604645
...
],
"y": [
0.6111111044883728,
0.6138888597488403,
0.6138888597488403
...
]
}
}
]
```
Pose Model Format
YOLO pose models, such as yolov8n-pose.pt, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
!!! example "Pose Model JSON Response"
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n-seg.pt')
# Run inference
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "CLI API"
```commandline
curl -X POST -F image=@image.jpg https://api.ultralytics.com/inference/v1?model=MODEL_ID,key=API_KEY
```
=== "Python API"
```python
import requests
api_key = "API_KEY"
model_id = "MODEL_ID"
url = "https://api.ultralytics.com/inference/v1"
# Define your query parameters
params = {
"key": api_key,
"model": model_id,
}
image_path = "image.jpg"
with open(image_path, "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, files=files, params=params)
print(response.text)
```
=== "JSON Response"
Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
```json
[
{
"name": "person",
"class": 0,
"confidence": 0.8439509868621826,
"box": {
"x1": 0.1125,
"y1": 0.28194444444444444,
"x2": 0.7953125,
"y2": 0.9902777777777778
},
"keypoints": {
"x": [
0.5058594942092896,
0.5103894472122192,
0.4920862317085266
...
],
"y": [
0.48964157700538635,
0.4643048942089081,
0.4465252459049225
...
],
"visible": [
0.8726999163627625,
0.653947651386261,
0.9130823612213135
...
]
}
},
{
"name": "person",
"class": 0,
"confidence": 0.7474289536476135,
"box": {
"x1": 0.58125,
"y1": 0.0625,
"x2": 0.8859375,
"y2": 0.9888888888888889
},
"keypoints": {
"x": [
0.778544008731842,
0.7976160049438477,
0.7530890107154846
...
],
"y": [
0.27595141530036926,
0.2378823608160019,
0.23644638061523438
...
],
"visible": [
0.8900790810585022,
0.789978563785553,
0.8974530100822449
...
]
}
}
]
```