Tesi

Basics of the project

The tags to launch this program are:

• -i to give a relative or absolute path to an image to ingest, if omitted default to the camera;
• -o to give the path to the output image, if omitted an image will pop up;
• -m to detect multiple people, if omitted default to single detection.
• -g to use gpu accelerated flow with OpenCv compiled with CUDA support (only with custom installation, tutorial below)
• -d select a directory to process (mutually exclusive with -i), -o behaviour changes to the output directory's name (requested)
• -p cover detected faces

OpenPose's License.

OpenPose's README.md.

Prerequisites

• opencv-python 4.5.1 compiled with CUDA11 and cudnn 8.5 Video tutorial to install it, Blog tutorial
• numpy

Support code

components/models/model_multiple.py

...

class MultipleDetectionsModel(Model):

...

    def get_keypoints(self, prob_map) -> List[Tuple[List[int], float]]:
        ...
        # to retrive the prob_maps for probMapsRetreiver.py .model.extrapolate_prob_map(prob_map)
        ...

...

    def get_valid_pairs(
            self, model_detections, frame_width: int, frame_height: int
        ) -> Tuple[List[NDArray], List[int]]:
        ...
        # to show the heatmaps run: .model.show_heatmap(paf_a,frame), .model.show_heatmap(paf_b,frame)
        # YOU NEED TO PASS THE FRAME AS AN ARGUMENT FROM find_detections
        ...

...

The implementations of extrapolate_prob_map and show_heatmap are in components/models/model.py

probMapsRetreiver.py is used to parse the debug output of the probability map you'll need to use:

extrapolate_prob_map(prob_map)

ProbMapPlotter.py can be used after running probMapsRetreiver and uses Matplotlib to display the different prob maps