Model Module
This module contain all methods to create and use a CSRNet model for crowd counting
- class Crowd_counting.model.CSRNet(*args: Any, **kwargs: Any)[source]
Bases:
ModuleThis class represents a CSRNet model
- Crowd_counting.model.evaluate(model, img_paths, MAE=True, MSE=True)[source]
Compute the MSE-MAE of the model on a designated set of images
- Parameters
model – a CSRNet already trained model
image_paths (list) – paths to the images used for evaluation
MAE (bool) – If set to False the MAE is not computed
MSE (bool) – If set to False the MSE is not computed
- Returns
the MAE of the model (0 if the MAE parameter is set to False)
- Returns
the MSE of the model (0 if the MSE parameter is set to False)
- Crowd_counting.model.load_model(model_path, use_gpu=True)[source]
Load a stored already trained CSRNet model.
- Parameters
model_path – the path to the stored model
use_gpu – if the model is loaded on gpu or cpu
- Returns
the loaded model
- Return type
- Crowd_counting.model.load_pretrained(model_name='shanghaiA')[source]
Load one of the already pretrained models of the librairy
- Parameters
model_name – name of the pretrained model to be charged. Can be shanghaiA, shanghaiB, A10
- Returns
the loaded model
- Return type
- Crowd_counting.model.predict(model, image_path, use_gpu=True)[source]
Predict the stimated number of people and density map from an image
- Parameters
model – a CSRNet already trained model
image_path – path to the image to be predicted
use_gpu – if the model is loaded on gpu or cpu
- Returns
the estimated number of people
- Returns
the estimated density map
- Crowd_counting.model.visualize(image, ground_truth=None, model=None, figsize=(100, 100))[source]
Visualize an image and eventually its ground_truth and one model prediction using matplotlib
- Parameters
image – the path to base image
ground_truth – a .h5 file corresponding to the ground_truth of the image
model – a model used to make a prediction on the base image