import cv2
import numpy as np
import imutils
import warnings
import os
from PIL import Image
warnings.filterwarnings("ignore")

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep","sofa", "train", "tvmonitor"]
    

def load_mobile_net():
    protopath = "MobileNetSSD_deploy.prototxt"
    modelpath = "MobileNetSSD_deploy.caffemodel"
    detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
    
    return detector

def load_image(image_path):
    img = cv2.imread(image_path)
    #img = cv2.resize(img,fx = 0.4,fy=0.4)
    Height,Width= img.shape[:2]
    
    return img,Height,Width

def find_dim(image_path):
    img = cv2.imread(image_path)
    Heigth,Width= img.shape[:2]
    
    return Height,Width

def detect_object(img,detector):
    
    blob = cv2.dnn.blobFromImage(img, 0.007843, (320,320), 127.5)
    detector.setInput(blob)
    person_detections = detector.forward()
    
    return blob,person_detections

def get_box_dimensions(person_detections):
    
    for i in np.arange(0, person_detections.shape[2]):
        bbox = []
        confidence = person_detections[0, 0, i, 2]
        
        
        if confidence > 0.5:
            idx = int(person_detections[0, 0, i, 1])

            if CLASSES[idx] != "person":
                continue

            bbox.append(person_detections[0, 0, i, 3:7] * np.array([800, 400, 800, 400]))
            (startX, startY, endX, endY) = person_box.astype("int")
            
    return bbox , confidence

def main():
    
    PATH_TO_TEST_IMAGES_DIR = 'testing'
    TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3)]
    
    for image_path in TEST_IMAGE_PATHS:
        
            model = load_mobile_net()
            image,H,W = load_image(image_path)
            blob,person_detections = detect_object(image,model)
            person_box , confidence = get_box_dimensions(person_detections)   

main()