import cv2
from pipline_functions import croped_images,object_detection,image_enhancements,detect_activity,get_distances,get_json_data
import os

def pipline(image):
    """_summary_

    Args:
        image (numpy array): get numpy array of image which has 3 channels

    Returns:
        final_results: JSON Array which has below object
        {
            'zoomed_img':np.array([]) ,
            'actual_boxes':[],
            'updated_boxes':{},
        }
    """
    # detect object of given image using YOLO and get json_data of each object
    json_data = object_detection(image)

    # get croped_images list which has overlapping boundry box and also get croped single object images
    croped_images_list,single_object_images=  croped_images(image,json_data)

    # enhance images of both croped images and single object images
    enhanced_images,single_object_images = image_enhancements(croped_images_list,single_object_images)

    # detect activity of person object using image classification
    detected_activity = detect_activity(single_object_images)

    # Calculate distances of all objects
    distances_list = get_distances(json_data)

    # get final json array 
    final_results = get_json_data(json_data,enhanced_images,detected_activity,distances_list)

    # print(distances_list)
    # print(detected_activity)
    # print(final_results)

    return final_results

pipline(cv2.imread('distance_test\distance_test\images\car_99-94168281555176_Mon-Dec-13-16-37-40-2021_jpg.rf.a8c56aba60dd3a19f2c2f159a2c9062d.jpg'))