model_tools.py

import cv2
import datetime
from matplotlib.colors import hsv_to_rgb
import torch
import numpy as np
from super_gradients.training import models
from super_gradients.training.models.detection_models.customizable_detector import CustomizableDetector
from super_gradients.training.pipelines.pipelines import DetectionPipeline

from deep_sort_torch.deep_sort.deep_sort import DeepSort
import os

# make sure to set IOU and confidence in the pipeline constructor

def get_color(number):
    """ Converts an integer number to a color """
    # change these however you want to
    hue = number*30 % 180
    saturation = number*103 % 256
    value = number*50 % 256

    # expects normalized values
    hsv_array = [hue/179, saturation/255, value/255]
    rgb = hsv_to_rgb(hsv_array)

    return [int(c*255) for c in rgb]

def img_predict(media, model, out_path,filename):
    save_to = os.path.join(out_path, filename)
    images_predictions = model.predict(media,conf=0.70,fuse_model=False)
    images_predictions.save(output_folder=out_path, box_thickness=2, show_confidence=True)

    return None

def get_prediction(model, image_in, pipeline):
    ''' Obtains DetectionPrediction object from a single input RGB image
    '''
    # Preprocess
    preprocessed_image, processing_metadata = pipeline.image_processor.preprocess_image(image=image_in.copy())

    # Predict
    with torch.no_grad():
        #torch_input = torch.Tensor(preprocessed_image).unsqueeze(0).to('cpu')
        torch_input = torch.Tensor(preprocessed_image).unsqueeze(0).to("cuda" if torch.cuda.is_available() else "cpu") 
        model_output = model(torch_input)
        prediction = pipeline._decode_model_output(model_output, model_input=torch_input)
  # Postprocess
    return pipeline.image_processor.postprocess_predictions(predictions=prediction[0], metadata=processing_metadata)