Implement New Models#
PocketPose offers a flexible and extensible framework for integrating various pose estimation models, particularly tailored for mobile devices. At its core, the library utilizes a well-defined interface for models, making it straightforward to add new models and extend the library’s capabilities.
To integrate a new model into PocketPose, follow these steps:
Inherit from IModel: Your model class should inherit from the
IModel
interface or one of its direct subclasses. This ensures consistency and interoperability within the PocketPose framework.from pocketpose.models.interfaces import IModel class YourCustomModel(IModel): ...
Implement Required Methods: Implement the abstract methods defined in the
IModel
interface. These typically includeprocess_image
,predict
, andpostprocess_prediction
.process_image
: Prepare the input image for prediction.predict
: Run the model inference.postprocess_prediction
: Process the raw model output to extract meaningful information, such as keypoint coordinates.
Add Custom Logic (if needed): Depending on your model’s specific requirements, you can add additional methods or override existing ones for customized behavior.
Integrate with Model Factory: Optionally, integrate your model with the ModelFactory to enable easy instantiation.
Example#
The following example demonstrates how to integrate a TFLite model into PocketPose.
from pocketpose.models.interfaces import TFLiteModel
from pocketpose.models.registry import model_registry
@model_registry.register('model_name')
class CustomModel(TFLiteModel):
def __init__(self,
model_path: str = "path/to/cache/model/file.tflite",
model_url: str = "https://url/to/download/model/from.tflite",
input_size: tuple = (192, 192, 3)):
super().__init__(model_path, model_url, keypoints_type='coco',
input_size=input_size, output_type='keypoints')
def postprocess_prediction(self, prediction, original_size):
keypoints = prediction.squeeze() # (17, 3) as (y, x, score)
keypoints[:, :2] *= original_size
keypoints = [tuple([int(x), int(y), s]) for y, x, s in keypoints]
return keypoints # (17, 3) as (x, y, score)
The decorated model_registry.register
method registers the model with the ModelFactory, allowing it to be instantiated by name. The postprocess_prediction
method converts the output from model-specific format to a list of keypoints, which is the expected output format for PocketPose.