import urllib
import time
import sys
import numpy as np
import cv2
from rknnlite.api import RKNNLite

RKNN_MODEL = './rknnModel/helmet_relu.rknn'
IMG_PATH = './bus.jpg'
OBJ_THRESH = 0.45
NMS_THRESH = 0.45
IMG_SIZE = 640
CLASSES = ("head", "helmet")


def sigmoid(x):
    return 1 / (1 + np.exp(-x))


def xywh2xyxy(x):
    # Convert [x, y, w, h] to [x1, y1, x2, y2]
    y = np.copy(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y


def process(input, mask, anchors):

    anchors = [anchors[i] for i in mask]
    grid_h, grid_w = map(int, input.shape[0:2])

    box_confidence = input[..., 4]
    box_confidence = np.expand_dims(box_confidence, axis=-1)

    box_class_probs = input[..., 5:]

    box_xy = input[..., :2] * 2 - 0.5

    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    grid = np.concatenate((col, row), axis=-1)
    box_xy += grid
    box_xy *= int(IMG_SIZE/grid_h)

    box_wh = pow(input[..., 2:4] * 2, 2)
    box_wh = box_wh * anchors

    return np.concatenate((box_xy, box_wh), axis=-1), box_confidence, box_class_probs


def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!

    # Arguments
        boxes: ndarray, boxes of objects.
        box_confidences: ndarray, confidences of objects.
        box_class_probs: ndarray, class_probs of objects.

    # Returns
        boxes: ndarray, filtered boxes.
        classes: ndarray, classes for boxes.
        scores: ndarray, scores for boxes.
    """
    boxes = boxes.reshape(-1, 4)
    box_confidences = box_confidences.reshape(-1)
    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])

    _box_pos = np.where(box_confidences >= OBJ_THRESH)
    boxes = boxes[_box_pos]
    box_confidences = box_confidences[_box_pos]
    box_class_probs = box_class_probs[_box_pos]

    class_max_score = np.max(box_class_probs, axis=-1)
    classes = np.argmax(box_class_probs, axis=-1)
    _class_pos = np.where(class_max_score >= OBJ_THRESH)

    return boxes[_class_pos], classes[_class_pos], (class_max_score * box_confidences)[_class_pos]


def nms_boxes(boxes, scores):
    """Suppress non-maximal boxes.

    # Arguments
        boxes: ndarray, boxes of objects.
        scores: ndarray, scores of objects.

    # Returns
        keep: ndarray, index of effective boxes.
    """
    x = boxes[:, 0]
    y = boxes[:, 1]
    w = boxes[:, 2] - boxes[:, 0]
    h = boxes[:, 3] - boxes[:, 1]

    areas = w * h
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)

        xx1 = np.maximum(x[i], x[order[1:]])
        yy1 = np.maximum(y[i], y[order[1:]])
        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])

        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
        inter = w1 * h1

        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        inds = np.where(ovr <= NMS_THRESH)[0]
        order = order[inds + 1]
    return np.array(keep)


def yolov5_post_process(input_data):
    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
               [59, 119], [116, 90], [156, 198], [373, 326]]

    boxes, classes, scores = [], [], []
    for input, mask in zip(input_data, masks):
        b, c, s = process(input, mask, anchors)
        print('abc',b,c,s)
        b, c, s = filter_boxes(b, c, s)
        boxes.append(b)
        classes.append(c)
        scores.append(s)

    boxes = np.concatenate(boxes)
    boxes = xywh2xyxy(boxes)
    classes = np.concatenate(classes)
    scores = np.concatenate(scores)

    nboxes, nclasses, nscores = [], [], []
    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]

        keep = nms_boxes(b, s)

        nboxes.append(b[keep])
        nclasses.append(c[keep])
        nscores.append(s[keep])

    if not nclasses and not nscores:
        return None, None, None

    return np.concatenate(nboxes), np.concatenate(nclasses), np.concatenate(nscores)


def draw(image, boxes, scores, classes):
    for box, score, cl in zip(boxes, scores, classes):
        top, left, right, bottom = box
        height, width, channels = image.shape
        print('height, width',height, width, channels)
        print('class: {}, score: {}'.format(CLASSES[cl], score))
        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top/height, left/width, right/width, bottom/height))
        
        top = int(top)
        left = int(left)
        right = int(right)
        bottom = int(bottom)
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (top, left - 6),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.6, (0, 0, 255), 2)


def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
    shape = im.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - \
        new_unpad[1]  # wh padding

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    im = cv2.copyMakeBorder(im, top, bottom, left, right,
                            cv2.BORDER_CONSTANT, value=color)  # add border
    return im
    # return im, ratio, (dw, dh)


if __name__ == '__main__':
    rknn = RKNNLite()

    print('--> Load RKNN model')
    ret = rknn.load_rknn(RKNN_MODEL)
    if ret != 0:
        print('Load RKNN model failed')
        exit(ret)
    print('done')
    ret = rknn.init_runtime()
    if ret != 0:
        print('Init runtime environment failed!')
        exit(ret)
    print('done')

    # capture = cv2.VideoCapture(
    #     'rtsp://admin:[email protected]:8554/Streaming/Channels/101')

    capture = cv2.VideoCapture(
        'rtsp://admin:[email protected]:554/11')

    ref, frame = capture.read()
    if not ref:
        raise ValueError("error reading")

    fps = 0.0
    while (True):
        t1 = time.time()
        #
        ref, frame = capture.read()
        if not ref:
            break
        # BGRtoRGB
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        #############
        #
        # img = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
        # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
        # Inference
        print('--> Running model')
        outputs = rknn.inference(inputs=[img])

        input0_data = outputs[0]
        input1_data = outputs[1]
        input2_data = outputs[2]

        input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
        input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
        input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))

        input_data = list()
        input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
        input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
        input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
        boxes, classes, scores = yolov5_post_process(input_data)

        img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        if boxes is not None:
            draw(img_1, boxes, scores, classes)

        fps = (fps + (1./(time.time()-t1))) / 2
        print("fps= %.2f" % (fps))
        # cv2.imshow("video", img_1)
        c = cv2.waitKey(1) & 0xff
        if c == 27:
            capture.release()
            break
    print("Video Detection Done!")
    capture.release()
    cv2.destroyAllWindows()