import cv2
import torch
import numpy as np
import onnxruntime as ort
from utils.general import non_max_suppression, scale_coords
from utils.BaseDetector import baseDet
from utils.datasets import letterbox
import logging

logging.basicConfig(filename='detection_log.txt', level=logging.INFO, 
                    format='%(asctime)s - %(message)s')

class Detector(baseDet):
    def __init__(self):
        super(Detector, self).__init__()
        self.device = None
        self.weights = None
        self.session = None
        self.names = None
        self.img_size = 640
        self.init_model()
        self.build_config()

    def init_model(self):
        self.weights = 'weights/yolov5s.onnx'
        self.device = '0' if torch.cuda.is_available() else 'cpu'
        #self.session = ort.InferenceSession(self.weights, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
        self.session = ort.InferenceSession(self.weights, providers=['CUDAExecutionProvider'])
        self.names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
                      'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
                      'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
                      'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
                      'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
                      'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
                      'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
                      'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
                      'hair drier', 'toothbrush']

    def preprocess(self, img):
        img0 = img.copy()
        img = cv2.resize(img, (640, 640))
        img = letterbox(img, new_shape=self.img_size)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)
        img = np.ascontiguousarray(img)
        img = img.astype(np.float32)
        img /= 255.0

        if img.ndim == 3:
            img = np.expand_dims(img, axis=0)

        return img0, img

    def detect(self, im):
        im0, img = self.preprocess(im)

        # Prepare input for ONNX model
        input_name = self.session.get_inputs()[0].name
        pred = self.session.run(None, {input_name: img})[0]  # Run inference

        pred = pred.astype(np.float32)
        pred = non_max_suppression(torch.from_numpy(pred), self.threshold, 0.4)

        pred_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', 'car', 'truck']:  # Filter unwanted labels
                        continue
                    x1, y1 = int(x[0]), int(x[1])
                    x2, y2 = int(x[2]), int(x[3])
                    pred_boxes.append((x1, y1, x2, y2, lbl, conf))

        return im0, pred_boxes