import torch
import numpy as np

from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import non_max_suppression, scale_coords
from utils.torch_utils import select_device


class Detector:

    def __init__(self, img_size=640, conf=0.01, iou=0.65, model_type='x6', old_init=False):
        if old_init:
            self.__old_init__()
        else:
            self.model = torch.hub.load('./weights/', 'yolov5{}'.format(model_type), pretrained=True, source='local')
            self.model.conf = conf
            self.model.iou = iou
            # self.model.classes = [0, 1, 2, 3, 5, 7, 8]

            self.img_size = img_size

    def predict(self, img):
        """im = cv2.imread(im_path)
        """
        results = self.model(img, size=self.img_size)
        results.render()
        for img in results.imgs:
            output_image_frame = img
        return output_image_frame, results.pandas().xyxy[0][['xmin', 'ymin', 'xmax', 'ymax', 'name', 'confidence']].values.tolist()

    def __old_init__(self):
        self.img_size = 640
        self.threshold = 0.3
        self.stride = 1



        self.weights = './weights/yolov5m.pt'

        self.device = '0' if torch.cuda.is_available() else 'cpu'
        self.device = select_device(self.device)
        # model = attempt_load(self.weights, map_location=self.device)
        model = torch.hub.load('./weights/', 'yolov5{}'.format(model_type), pretrained=True, source='local')
        model.to(self.device).eval()
        model.half()

        self.m = model
        self.names = model.module.names if hasattr(
            model, 'module') else model.names


    def preprocess(self, img):

        img0 = img.copy()
        img = letterbox(img, new_shape=self.img_size)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)
        img = np.ascontiguousarray(img)
        img = torch.from_numpy(img).to(self.device)
        img = img.half()
        img /= 255.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        return img0, img

    def detect(self, im):

        im0, img = self.preprocess(im)

        pred = self.m(img, augment=False)[0]
        pred = pred.float()
        pred = non_max_suppression(pred, self.threshold, 0.4)

        boxes = []
        for det in pred:

            if det is not None and len(det):
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape).round()

                for *x, conf, cls_id in det:
                    lbl = self.names[int(cls_id)]
                    if lbl not in ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck']:
                        continue
                    pass
                    x1, y1 = int(x[0]), int(x[1])
                    x2, y2 = int(x[2]), int(x[3])
                    boxes.append(
                        (x1, y1, x2, y2, lbl, conf))

        return boxes