import cv2
import numpy as np
import argparse
import time

parser = argparse.ArgumentParser()
parser.add_argument('--webcam', help="True/False", default=False)
parser.add_argument('--play_video', help="Tue/False", default=False)
parser.add_argument('--image', help="Tue/False", default=False)
parser.add_argument('--video_path', help="Path of video file", default="videos/car_on_road.mp4")
parser.add_argument('--image_path', help="Path of image to detect objects", default="Images/bicycle.jpg")
parser.add_argument('--verbose', help="To print statements", default=True)
args = parser.parse_args()

#Load yolo
def load_yolo():
    #net = cv2.dnn.readNet("yolov3-tiny.weights", "yolov3-tiny.cfg")
    net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
    classes = []
    with open("coco.names", "r") as f:
        classes = [line.strip() for line in f.readlines()]

    output_layers = [layer_name for layer_name in net.getUnconnectedOutLayersNames()]
    colors = np.random.uniform(0, 255, size=(len(classes), 3))
    return net, classes, colors, output_layers

def load_image(img_path):
    # image loading
    img = cv2.imread(img_path)
    img = cv2.resize(img, None, fx=0.4, fy=0.4)
    height, width, channels = img.shape
    return img, height, width, channels

def start_webcam():
    cap = cv2.VideoCapture(0)

    return cap


def display_blob(blob):
    '''
    Three images each for RED, GREEN, BLUE channel
    '''
    for b in blob:
        for n, imgb in enumerate(b):
            cv2.imshow(str(n), imgb)

def detect_objects(img, net, outputLayers):
    blob = cv2.dnn.blobFromImage(img, scalefactor=0.00392, size=(320, 320), mean=(0, 0, 0), swapRB=True, crop=False)
    net.setInput(blob)
    outputs = net.forward(outputLayers)
    return blob, outputs

def get_box_dimensions(outputs, height, width):
    boxes = []
    confs = []
    class_ids = []
    for output in outputs:
        for detect in output:
            scores = detect[5:]
            class_id = np.argmax(scores)
            conf = scores[class_id]
            if conf > 0.3:
                center_x = int(detect[0] * width)
                center_y = int(detect[1] * height)
                w = int(detect[2] * width)
                h = int(detect[3] * height)
                x = int(center_x - w/2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confs.append(float(conf))
                class_ids.append(class_id)
    return boxes, confs, class_ids

def draw_labels(boxes, confs, colors, class_ids, classes, img):
    indexes = cv2.dnn.NMSBoxes(boxes, confs, 0.5, 0.4)
    font = cv2.FONT_HERSHEY_PLAIN
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            color = colors[i]
            cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
            cv2.putText(img, label, (x, y - 5), font, 3, color, 3)
    cv2.imshow("Image", img)

def image_detect(img_path):
    model, classes, colors, output_layers = load_yolo()
    image, height, width, channels = load_image(img_path)
    blob, outputs = detect_objects(image, model, output_layers)
    boxes, confs, class_ids = get_box_dimensions(outputs, height, width)
    draw_labels(boxes, confs, colors, class_ids, classes, image)
    while True:
        key = cv2.waitKey(1)
        if key == 27:
            break

def webcam_detect():
    model, classes, colors, output_layers = load_yolo()
    cap = start_webcam()
    while True:
        _, frame = cap.read()
        height, width, channels = frame.shape
        blob, outputs = detect_objects(frame, model, output_layers)
        boxes, confs, class_ids = get_box_dimensions(outputs, height, width)
        draw_labels(boxes, confs, colors, class_ids, classes, frame)
        key = cv2.waitKey(1)
        if key == 27:
            break
    cap.release()


def start_video(video_path):
    model, classes, colors, output_layers = load_yolo()
    cap = cv2.VideoCapture(video_path)
    while True:
        _, frame = cap.read()
        height, width, channels = frame.shape
        blob, outputs = detect_objects(frame, model, output_layers)
        boxes, confs, class_ids = get_box_dimensions(outputs, height, width)
        draw_labels(boxes, confs, colors, class_ids, classes, frame)
        k = cv2.waitKey(1) & 0xFF
        if k == 27:
            cv2.destroyAllWindows()
            break
    cap.release()



if __name__ == '__main__':
    webcam = args.webcam
    video_play = args.play_video
    image = args.image
    if webcam:
        if args.verbose:
            print('---- Starting Web Cam object detection ----')
        webcam_detect()
    if video_play:
        video_path = args.video_path
        if args.verbose:
            print('Opening '+video_path+" .... ")
        start_video(video_path)
    if image:
        image_path = args.image_path
        if args.verbose:
            print("Opening "+image_path+" .... ")
        image_detect(image_path)


    cv2.destroyAllWindows()