#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.

from loguru import logger

import cv2
import numpy as np
import torch

from yolox.data.data_augment import preproc
from yolox.data.datasets import COCO_CLASSES
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis

import argparse
import os
import time
from numpy import random
import cvzone


palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)



# Draws the disconnected Rectangle around the object
def draw_disconnected_rect( img, pt1, pt2, color, thickness):
    width = pt2[0] - pt1[0]
    height = pt2[1] - pt1[1]
    line_width = min(30, width // 3)
    line_height = min(30, height // 3)
    line_length = max(line_width, line_height)
    cv2.line(img, pt1, (pt1[0] + line_length, pt1[1]), color, thickness)
    cv2.line(img, pt1, (pt1[0], pt1[1] + line_length), color, thickness)
    cv2.line(
        img, (pt2[0] - line_length, pt1[1]), (pt2[0], pt1[1]), color, thickness
    )
    cv2.line(
        img, (pt2[0], pt1[1]), (pt2[0], pt1[1] + line_length), color, thickness
    )
    cv2.line(
        img, (pt1[0], pt2[1]), (pt1[0] + line_length, pt2[1]), color, thickness
    )
    cv2.line(
        img, (pt1[0], pt2[1] - line_length), (pt1[0], pt2[1]), color, thickness
    )
    cv2.line(img, pt2, (pt2[0] - line_length, pt2[1]), color, thickness)
    cv2.line(img, (pt2[0], pt2[1] - line_length), pt2, color, thickness)



# Draws the shape of label component
def draw_border(img, pt1, pt2, color, thickness, r, d):
    x1,y1 = pt1
    x2,y2 = pt2
    # Top left
    cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
    cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
    cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)

    # Top right
    cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
    cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
    cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
    # Bottom left
    cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
    cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
    cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
    # Bottom right
    cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
    cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
    cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)

    cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA)
    cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA)
    
    cv2.circle(img, (x1 +r, y1+r), 2, color, 12)
    cv2.circle(img, (x2 -r, y1+r), 2, color, 12)
    cv2.circle(img, (x1 +r, y2-r), 2, color, 12)
    cv2.circle(img, (x2 -r, y2-r), 2, color, 12)    
    return img

# Implementing the label component
def UI_box2(x, img, color=None,label=None,line_thickness=None, boundingbox = True):
    # Plots one bounding box on image img
    tl = line_thickness or round(0.30 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    if boundingbox:
        # cv2.rectangle(img, c1, c2, color, 2)
        draw_disconnected_rect(img, c1, c2, color, tl)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        img = draw_border(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color, tf, 8, 2)
        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
    # return img


# Converting emojis to Frames
def vid_to_frames(path):
    frames = []
    cap = cv2.VideoCapture(path)
    ret = True
    while ret:
        ret, img = cap.read() # read one frame from the 'capture' object; img is (H, W, C)
        if ret:
            frames.append(img)
    return frames

# Emoji Dict to add more emojis make sure they follow naming conventions as per your labels
# For this to wrok your custom trainig has to be done as mentioned in the lectures

emojidict = dict(
    happy = vid_to_frames('assets/happy.png'),
    sad = vid_to_frames('assets/sad.png'),
    surprised = vid_to_frames('assets/surprised.png')       
    )

current = None
count = 0


# Overlaying Image and Emoji
def add_image(img, src2, x, y, ):
    # x=  x+90
    # y = y-10
    w = 80
    h = 80

    initial = img[y:y+h,x:x+w]
    src1 = initial
    src2 = cv2.resize(src2, src1.shape[1::-1])
    u_green = np.array([1, 1, 1])
    l_green = np.array([0, 0, 0])
    mask = cv2.inRange(src2, l_green, u_green)
    res = cv2.bitwise_and(src2, src2, mask = mask)
    f = src2 - res
    f = np.where(f == 0, src1, f)
    img[y:y+h,x:x+w] = f
    return img

def compute_color_for_labels(label):
    """
    Simple function that adds fixed color depending on the class
    """
    if label == 0: 
        color = (85,45,255)
    elif label == 1:
        color = (222,82,175)
    elif label == 2:
        color = (0, 204, 255)
    else:
        color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
    return tuple(color)

# Combining all the elements and returning the final image
def vis2(img, boxes, scores, cls_ids, conf=0.5, class_names=None):

    global current
    global count

    for i in range(len(boxes)):
        box = boxes[i]
        cls_id = int(cls_ids[i])
        score = scores[i]
        if score < conf:
            continue
        label = class_names[cls_id]
        
        UI_box2(box, img, color=compute_color_for_labels(cls_id),label=label,line_thickness=2)
        if count == len(emojidict[label]):
            count = 0 
        if label == current:
            count +=1
        elif label != current:
            count = 0 
        width = int(box[0]) - int(box[0])
        height = int(box[1]) - int(box[1])
        try:
            img = add_image(img, emojidict[label][count] ,int(box[0] + width/2), int(box[1]) )
        except:
            img = add_image(img, emojidict[label][0] ,int(box[0]), int(box[1]) )

        current = label
        
    return img


IMAGE_EXT = [".jpg", ".jpeg", ".webp", ".bmp", ".png"]


def make_parser():
    parser = argparse.ArgumentParser("YOLOX Demo!")
    parser.add_argument(
        "demo", default="image", help="demo type, eg. image, video and webcam"
    )
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default=None, help="model name")

    parser.add_argument(
        "--path", default="./assets/dog.jpg", help="path to images or video"
    )
    parser.add_argument("--camid", type=int, default=0, help="webcam demo camera id")
    parser.add_argument(
        "--save_result",
        action="store_true",
        help="whether to save the inference result of image/video",
    )

    # exp file
    parser.add_argument(
        "-f",
        "--exp_file",
        default=None,
        type=str,
        help="pls input your expriment description file",
    )
    parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt for eval")
    parser.add_argument(
        "--device",
        default="cpu",
        type=str,
        help="device to run our model, can either be cpu or gpu",
    )
    parser.add_argument("--conf", default=None, type=float, help="test conf")
    parser.add_argument("--nms", default=None, type=float, help="test nms threshold")
    parser.add_argument("--tsize", default=None, type=int, help="test img size")
    parser.add_argument(
        "--fp16",
        dest="fp16",
        default=False,
        action="store_true",
        help="Adopting mix precision evaluating.",
    )
    parser.add_argument(
        "--fuse",
        dest="fuse",
        default=False,
        action="store_true",
        help="Fuse conv and bn for testing.",
    )
    parser.add_argument(
        "--trt",
        dest="trt",
        default=False,
        action="store_true",
        help="Using TensorRT model for testing.",
    )
    return parser


def get_image_list(path):
    image_names = []
    for maindir, subdir, file_name_list in os.walk(path):
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)
            ext = os.path.splitext(apath)[1]
            if ext in IMAGE_EXT:
                image_names.append(apath)
    return image_names


class Predictor(object):
    def __init__(
        self,
        model,
        exp,
        cls_names=COCO_CLASSES,
        trt_file=None,
        decoder=None,
        device="cpu",
    ):
        self.model = model
        self.cls_names = cls_names
        self.decoder = decoder
        self.num_classes = exp.num_classes
        self.confthre = exp.test_conf
        self.nmsthre = exp.nmsthre
        self.test_size = exp.test_size
        self.device = device
        if trt_file is not None:
            from torch2trt import TRTModule

            model_trt = TRTModule()
            model_trt.load_state_dict(torch.load(trt_file))

            x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda()
            self.model(x)
            self.model = model_trt
        self.rgb_means = (0.485, 0.456, 0.406)
        self.std = (0.229, 0.224, 0.225)

    def inference(self, img):
        img_info = {"id": 0}
        if isinstance(img, str):
            img_info["file_name"] = os.path.basename(img)
            img = cv2.imread(img)
        else:
            img_info["file_name"] = None

        height, width = img.shape[:2]
        img_info["height"] = height
        img_info["width"] = width
        img_info["raw_img"] = img

        img, ratio = preproc(img, self.test_size, self.rgb_means, self.std)
        img_info["ratio"] = ratio
        img = torch.from_numpy(img).unsqueeze(0)
        if self.device == "gpu":
            img = img.cuda()

        with torch.no_grad():
            t0 = time.time()
            outputs = self.model(img)
            if self.decoder is not None:
                outputs = self.decoder(outputs, dtype=outputs.type())
            outputs = postprocess(
                outputs, self.num_classes, self.confthre, self.nmsthre
            )
            logger.info("Infer time: {:.4f}s".format(time.time() - t0))
        return outputs, img_info

    def visual(self, output, img_info, cls_conf=0.35):
        ratio = img_info["ratio"]
        img = img_info["raw_img"]
        if output is None:
            return img
        output = output.cpu()

        bboxes = output[:, 0:4]

        # preprocessing: resize
        bboxes /= ratio

        cls = output[:, 6]
        scores = output[:, 4] * output[:, 5]

        vis_res = vis(img, bboxes, scores, cls, cls_conf, self.cls_names)
        
        return vis_res


def image_demo(predictor, vis_folder, path, current_time, save_result):
    if os.path.isdir(path):
        files = get_image_list(path)
    else:
        files = [path]
    files.sort()
    for image_name in files:
        outputs, img_info = predictor.inference(image_name)
        result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
        if save_result:
            save_folder = os.path.join(
                vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
            )
            os.makedirs(save_folder, exist_ok=True)
            save_file_name = os.path.join(save_folder, os.path.basename(image_name))
            logger.info("Saving detection result in {}".format(save_file_name))
            cv2.imwrite(save_file_name, result_image)
        ch = cv2.waitKey(0)
        if ch == 27 or ch == ord("q") or ch == ord("Q"):
            break


def imageflow_demo(predictor, vis_folder, current_time, args):
    cap = cv2.VideoCapture(args.path if args.demo == "video" else args.camid)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
    fps = cap.get(cv2.CAP_PROP_FPS)
    save_folder = os.path.join(
        vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
    )
    os.makedirs(save_folder, exist_ok=True)
    if args.demo == "video":
        save_path = os.path.join(save_folder, args.path.split("/")[-1])
    else:
        save_path = os.path.join(save_folder, "camera.mp4")
    logger.info(f"video save_path is {save_path}")
    vid_writer = cv2.VideoWriter(
        save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
    )
    while True:
        ret_val, frame = cap.read()
        if ret_val:
            outputs, img_info = predictor.inference(frame)
            result_frame = predictor.visual(outputs[0], img_info, predictor.confthre)
            if args.save_result:
                vid_writer.write(result_frame)
            ch = cv2.waitKey(1)
            if ch == 27 or ch == ord("q") or ch == ord("Q"):
                break
        else:
            break


def main(exp, args):
    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    file_name = os.path.join(exp.output_dir, args.experiment_name)
    os.makedirs(file_name, exist_ok=True)

    if args.save_result:
        vis_folder = os.path.join(file_name, "vis_res")
        os.makedirs(vis_folder, exist_ok=True)

    if args.trt:
        args.device = "gpu"

    logger.info("Args: {}".format(args))

    if args.conf is not None:
        exp.test_conf = args.conf
    if args.nms is not None:
        exp.nmsthre = args.nms
    if args.tsize is not None:
        exp.test_size = (args.tsize, args.tsize)

    model = exp.get_model()
    logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size)))

    if args.device == "gpu":
        model.cuda()
    model.eval()

    if not args.trt:
        if args.ckpt is None:
            ckpt_file = os.path.join(file_name, "best_ckpt.pth.tar")
        else:
            ckpt_file = args.ckpt
        logger.info("loading checkpoint")
        ckpt = torch.load(ckpt_file, map_location="cpu")
        # load the model state dict
        model.load_state_dict(ckpt["model"])
        logger.info("loaded checkpoint done.")

    if args.fuse:
        logger.info("\tFusing model...")
        model = fuse_model(model)

    if args.trt:
        assert not args.fuse, "TensorRT model is not support model fusing!"
        trt_file = os.path.join(file_name, "model_trt.pth")
        assert os.path.exists(
            trt_file
        ), "TensorRT model is not found!\n Run python3 tools/trt.py first!"
        model.head.decode_in_inference = False
        decoder = model.head.decode_outputs
        logger.info("Using TensorRT to inference")
    else:
        trt_file = None
        decoder = None

    predictor = Predictor(model, exp, COCO_CLASSES, trt_file, decoder, args.device)
    current_time = time.localtime()
    if args.demo == "image":
        image_demo(predictor, vis_folder, args.path, current_time, args.save_result)
    elif args.demo == "video" or args.demo == "webcam":
        imageflow_demo(predictor, vis_folder, current_time, args)


if __name__ == "__main__":
    args = make_parser().parse_args()
    exp = get_exp(args.exp_file, args.name)

    main(exp, args)
