import sys, os
import keras
import cv2
import traceback

from src.keras_utils import load_model
from glob import glob
from os.path import splitext, basename
from src.utils import im2single
from src.keras_utils import load_model, detect_lp
from src.label import Shape, writeShapes


def adjust_pts(pts, lroi):
    return pts * lroi.wh().reshape((2, 1)) + lroi.tl().reshape((2, 1))


path = None


def detection(img_path, output_dir, wpod_net_path):
    # input_dir = 'D:\\TEMP_Work\\license_work\\alpr-unconstrained\\samples\\input_test'
    # output_dir = output_dir

    lp_threshold = .5

    # wpod_net_path = 'D:\\TEMP_Work\\license_work\\alpr-unconstrained\\data\\lp-detector\wpod-net_final'
    wpod_net = load_model(wpod_net_path)

    print('Searching for license plates using WPOD-NET')

    print('\t Processing %s' % img_path)

    bname = splitext(basename(img_path))[0]
    Ivehicle = cv2.imread(img_path)

    ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.shape[:2])
    side = int(ratio * 288.)
    bound_dim = min(side + (side % (2 ** 4)), 608)
    print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))

    Llp, LlpImgs, _ = detect_lp(wpod_net, im2single(Ivehicle), bound_dim, 2 ** 4, (240, 80), lp_threshold)

    if len(LlpImgs):
        Ilp = LlpImgs[0]
        Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
        Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)

        s = Shape(Llp[0].pts)

        image = Ilp * 255.
        path = '%s/%s_lp.png' % (output_dir, bname)
        print(path)
        # cv2.imshow("detection", image)
        # cv2.waitKey()
        cv2.imwrite('%s/%s_lp.png' % (output_dir, bname), image)
    # writeShapes('%s/%s_lp.txt' % (output_dir, bname), [s])

    return path


# path = detection("D:\\TEMP_Work\\License_Plate_Recognition\\pic\\1.jpg", "D:\\TEMP_Work\\License_Plate_Recognition",
#                  "D:\\TEMP_Work\\License_Plate_Recognition\\lp-detector\\wpod-net_update1")
#
# print(path)

def detection_1():
    input_dir = 'D:\\TEMP_Work\\license_work\\alpr-unconstrained\\samples\\input_test'
    output_dir = input_dir

    lp_threshold = .5

    wpod_net_path = 'D:\\TEMP_Work\\License_Plate_Recognition\\lp-detector\\wpod-net_update1'
    wpod_net = load_model(wpod_net_path)

    imgs_paths = glob('%s/*.jpg' % input_dir)

    print('Searching for license plates using WPOD-NET')

    for i, img_path in enumerate(imgs_paths):

        print('\t Processing %s' % img_path)

        bname = splitext(basename(img_path))[0]
        Ivehicle = cv2.imread(img_path)

        ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.shape[:2])
        side = int(ratio * 288.)
        bound_dim = min(side + (side % (2 ** 4)), 608)
        print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))

        Llp, LlpImgs, _ = detect_lp(wpod_net, im2single(Ivehicle), bound_dim, 2 ** 4, (240, 80), lp_threshold)

        if len(LlpImgs):
            Ilp = LlpImgs[0]
            Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
            Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)

            s = Shape(Llp[0].pts)

            cv2.imwrite('%s/%s_lp.png' % (output_dir, bname), Ilp * 255.)
            writeShapes('%s/%s_lp.txt' % (output_dir, bname), [s])

# input_dir = 'D:\\TEMP_Work\\license_work\\alpr-unconstrained\\samples\\input_test'
# wpod_net_path = 'D:\\TEMP_Work\\License_Plate_Recognition\\lp-detector\\wpod-net_final'
# detection_1()

# if __name__ == '__main__':

# 	try:

# 		input_dir = 'D:\\TEMP_Work\\license_work\\alpr-unconstrained\\samples\\input_test'
# 		output_dir = input_dir

# 		lp_threshold = .5

# 		wpod_net_path = 'D:\\TEMP_Work\\license_work\\alpr-unconstrained\\data\\lp-detector\wpod-net_final'
# 		wpod_net = load_model(wpod_net_path)

# 		imgs_paths = glob('%s/*.jpg' % input_dir)

# 		print('Searching for license plates using WPOD-NET')

# 		for i, img_path in enumerate(imgs_paths):

# 			print('\t Processing %s' % img_path)

# 			bname = splitext(basename(img_path))[0]
# 			Ivehicle = cv2.imread(img_path)

# 			ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.shape[:2])
# 			side = int(ratio * 288.)
# 			bound_dim = min(side + (side % (2 ** 4)), 608)
# 			print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))

# 			Llp, LlpImgs, _ = detect_lp(wpod_net, im2single(Ivehicle), bound_dim, 2 ** 4, (240, 80), lp_threshold)

# 			if len(LlpImgs):
# 				Ilp = LlpImgs[0]
# 				Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
# 				Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)

# 				s = Shape(Llp[0].pts)

# 				cv2.imwrite('%s/%s_lp.png' % (output_dir, bname), Ilp * 255.)
# 				writeShapes('%s/%s_lp.txt' % (output_dir, bname), [s])

# 	except:
# 		traceback.print_exc()
# 		sys.exit(1)

# 	sys.exit(0)