import numpy as np
import cv2 as cv
import argparse

from yolox import YoloX

# Check OpenCV version
assert cv.__version__ >= "4.7.0", \
       "Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"

# Valid combinations of backends and targets
backend_target_pairs = [
    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
]

classes = ('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 letterbox(srcimg, target_size=(640, 640)):
    padded_img = np.ones((target_size[0], target_size[1], 3)) * 114.0
    ratio = min(target_size[0] / srcimg.shape[0], target_size[1] / srcimg.shape[1])
    resized_img = cv.resize(
        srcimg, (int(srcimg.shape[1] * ratio), int(srcimg.shape[0] * ratio)), interpolation=cv.INTER_LINEAR
    ).astype(np.float32)
    padded_img[: int(srcimg.shape[0] * ratio), : int(srcimg.shape[1] * ratio)] = resized_img

    return padded_img, ratio

def unletterbox(bbox, letterbox_scale):
    return bbox / letterbox_scale

def vis(dets, srcimg, letterbox_scale, fps=None):
    res_img = srcimg.copy()

    if fps is not None:
        fps_label = "FPS: %.2f" % fps
        cv.putText(res_img, fps_label, (10, 25), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    for det in dets:
        box = unletterbox(det[:4], letterbox_scale).astype(np.int32)
        score = det[-2]
        cls_id = int(det[-1])

        x0, y0, x1, y1 = box

        text = '{}:{:.1f}%'.format(classes[cls_id], score * 100)
        font = cv.FONT_HERSHEY_SIMPLEX
        txt_size = cv.getTextSize(text, font, 0.4, 1)[0]
        cv.rectangle(res_img, (x0, y0), (x1, y1), (0, 255, 0), 2)
        cv.rectangle(res_img, (x0, y0 + 1), (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), (255, 255, 255), -1)
        cv.putText(res_img, text, (x0, y0 + txt_size[1]), font, 0.4, (0, 0, 0), thickness=1)

    return res_img

if __name__=='__main__':
    parser = argparse.ArgumentParser(description='Nanodet inference using OpenCV an contribution by Sri Siddarth Chakaravarthy part of GSOC_2022')
    parser.add_argument('--input', '-i', type=str,
                        help='Path to the input image. Omit for using default camera.')
    parser.add_argument('--model', '-m', type=str, default='object_detection_yolox_2022nov.onnx',
                        help="Path to the model")
    parser.add_argument('--backend_target', '-bt', type=int, default=0,
                    help='''Choose one of the backend-target pair to run this demo:
                        {:d}: (default) OpenCV implementation + CPU,
                        {:d}: CUDA + GPU (CUDA),
                        {:d}: CUDA + GPU (CUDA FP16),
                        {:d}: TIM-VX + NPU,
                        {:d}: CANN + NPU
                    '''.format(*[x for x in range(len(backend_target_pairs))]))
    parser.add_argument('--confidence', default=0.5, type=float,
                        help='Class confidence')
    parser.add_argument('--nms', default=0.5, type=float,
                        help='Enter nms IOU threshold')
    parser.add_argument('--obj', default=0.5, type=float,
                        help='Enter object threshold')
    parser.add_argument('--save', '-s', action='store_true',
                        help='Specify to save results. This flag is invalid when using camera.')
    parser.add_argument('--vis', '-v', action='store_true',
                        help='Specify to open a window for result visualization. This flag is invalid when using camera.')
    args = parser.parse_args()

    backend_id = backend_target_pairs[args.backend_target][0]
    target_id = backend_target_pairs[args.backend_target][1]

    model_net = YoloX(modelPath= args.model,
                      confThreshold=args.confidence,
                      nmsThreshold=args.nms,
                      objThreshold=args.obj,
                      backendId=backend_id,
                      targetId=target_id)

    tm = cv.TickMeter()
    tm.reset()
    if args.input is not None:
        image = cv.imread(args.input)
        input_blob = cv.cvtColor(image, cv.COLOR_BGR2RGB)
        input_blob, letterbox_scale = letterbox(input_blob)

        # Inference
        tm.start()
        preds = model_net.infer(input_blob)
        tm.stop()
        print("Inference time: {:.2f} ms".format(tm.getTimeMilli()))

        img = vis(preds, image, letterbox_scale)

        if args.save:
            print('Resutls saved to result.jpg\n')
            cv.imwrite('result.jpg', img)

        if args.vis:
            cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
            cv.imshow(args.input, img)
            cv.waitKey(0)

    else:
        print("Press any key to stop video capture")
        deviceId = 0
        cap = cv.VideoCapture(deviceId)

        while cv.waitKey(1) < 0:
            hasFrame, frame = cap.read()
            if not hasFrame:
                print('No frames grabbed!')
                break

            input_blob = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
            input_blob, letterbox_scale = letterbox(input_blob)

            # Inference
            tm.start()
            preds = model_net.infer(input_blob)
            tm.stop()

            img = vis(preds, frame, letterbox_scale, fps=tm.getFPS())

            cv.imshow("YoloX Demo", img)

            tm.reset()
