wehub-resource-sync
69 lines
2.0 KiB
Markdown
69 lines
2.0 KiB
Markdown
# Objects
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The Objects pipeline reads a list of images and returns a list of detected objects.
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## Example
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The following shows a simple example using this pipeline.
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```python
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from txtai.pipeline import Objects
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# Create and run pipeline
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objects = Objects()
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objects("path to image file")
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```
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See the link below for a more detailed example.
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| Notebook | Description | |
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|:----------|:-------------|------:|
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| [Generate image captions and detect objects](https://github.com/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | Captions and object detection for images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) |
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## Configuration-driven example
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Pipelines are run with Python or configuration. Pipelines can be instantiated in [configuration](../../../api/configuration/#pipeline) using the lower case name of the pipeline. Configuration-driven pipelines are run with [workflows](../../../workflow/#configuration-driven-example) or the [API](../../../api#local-instance).
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### config.yml
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```yaml
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# Create pipeline using lower case class name
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objects:
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# Run pipeline with workflow
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workflow:
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objects:
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tasks:
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- action: objects
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```
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### Run with Workflows
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```python
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from txtai import Application
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# Create and run pipeline with workflow
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app = Application("config.yml")
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list(app.workflow("objects", ["path to image file"]))
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```
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### Run with API
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```bash
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CONFIG=config.yml uvicorn "txtai.api:app" &
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curl \
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-X POST "http://localhost:8000/workflow" \
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-H "Content-Type: application/json" \
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-d '{"name":"objects", "elements":["path to image file"]}'
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```
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## Methods
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Python documentation for the pipeline.
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### ::: txtai.pipeline.Objects.__init__
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### ::: txtai.pipeline.Objects.__call__
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