from __future__ import division, print_function

import os

import tensorflow as tf
import numpy as np
import argparse
import cv2

from utils.misc_utils import parse_anchors, read_class_names
from utils.nms_utils import gpu_nms
from utils.plot_utils import get_color_table, plot_one_box
from utils.data_aug import letterbox_resize

from model import yolov3

path = '/home/ml/Downloads/old/YOLOv3_TensorFlow/data/my_input/'
for i in os.listdir(path):
    parser = argparse.ArgumentParser(description="YOLO-V3 test single image test procedure.")
    parser.add_argument("--input_image", type=str, default=path+i,
                        help="The path of the input image.")
    parser.add_argument("--anchor_path", type=str, default="/home/ml/Downloads/old/YOLOv3_TensorFlow/data/yolo_anchors.txt",
                        help="The path of the anchor txt file.")
    parser.add_argument("--new_size", nargs='*', type=int, default=[416, 416],
                        help="Resize the input image with `new_size`, size format: [width, height]")
    parser.add_argument("--letterbox_resize", type=lambda x: (str(x).lower() == 'true'), default=True,
                        help="Whether to use the letterbox resize.")
    parser.add_argument("--class_name_path", type=str, default="/home/ml/Downloads/old/YOLOv3_TensorFlow/data/data.names",
                        help="The path of the class names.")
    parser.add_argument("--restore_path", type=str, default="/home/ml/Downloads/old/YOLOv3_TensorFlow/data/darknet_weightsbest_model_Epoch_68_step_621_mAP_0.9167_loss_2.0931_lr_1e-05",
                        help="The path of the weights to restore.")
    args = parser.parse_args()

    args.anchors = parse_anchors(args.anchor_path)
    args.classes = read_class_names(args.class_name_path)
    args.num_class = len(args.classes)

    color_table = get_color_table(args.num_class)

    img_ori = cv2.imread(args.input_image)
    if args.letterbox_resize:
        img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0], args.new_size[1])
    else:
        height_ori, width_ori = img_ori.shape[:2]
        img = cv2.resize(img_ori, tuple(args.new_size))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = np.asarray(img, np.float32)
    img = img[np.newaxis, :] / 255.

    with tf.Session() as sess:
        input_data = tf.placeholder(tf.float32, [1, args.new_size[1], args.new_size[0], 3], name='input_data')
        yolo_model = yolov3(args.num_class, args.anchors)
        with tf.variable_scope('yolov3', reuse=True) as yolov3:
            pred_feature_maps = yolo_model.forward(input_data, False, reuse=False)
        pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)

        pred_scores = pred_confs * pred_probs

        boxes, scores, labels = gpu_nms(pred_boxes, pred_scores, args.num_class, max_boxes=200, score_thresh=0.1,
                                        nms_thresh=0.45)

        saver = tf.train.Saver()
        saver.restore(sess, args.restore_path)
        boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})

        # rescale the coordinates to the original image
        if args.letterbox_resize:
            boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
            boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
        else:
            boxes_[:, [0, 2]] *= (width_ori/float(args.new_size[0]))
            boxes_[:, [1, 3]] *= (height_ori/float(args.new_size[1]))

        print("box coords:")
        print(boxes_)
        print('*' * 30)
        print("scores:")
        print(scores_)
        print('*' * 30)
        print("labels:")
        print(labels_)

        crop_img = img_ori[boxes_[0][1].astype('int'):boxes_[0][3].astype('int'), boxes_[0][0].astype('int'):boxes_[0][2].astype('int')]
        # cv2.imshow("cropped", crop_img)

        for j in range(len(boxes_)):
            x0, y0, x1, y1 = boxes_[j]
            plot_one_box(img_ori, [x0, y0, x1, y1], label=args.classes[labels_[j]] + ', {:.2f}%'.format(scores_[j] * 100), color=color_table[labels_[j]])
        # cv2.imshow('Detection result', img_ori)
        cv2.imwrite(f'detection_{i}', img_ori)
        cv2.imwrite(f'crop_{i}', crop_img)

cv2.waitKey(0)

with tf.variable_scope('yolov3', reuse=True) as yolov3:
            pred_feature_maps = yolo_model.forward(input_data, False, reuse=True)