import os

from params import args

os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu

from keras.preprocessing.image import img_to_array, load_img

from keras.applications.imagenet_utils import preprocess_input

from models.model_factory import make_model

from os import path, mkdir, listdir
import numpy as np

np.random.seed(1)
import random

random.seed(1)
import tensorflow as tf

tf.set_random_seed(1)
import timeit
import cv2
from tqdm import tqdm

test_folder = args.test_folder
test_pred = os.path.join(args.out_root_dir, args.out_masks_folder)

all_ids = []
all_images = []
all_masks = []

OUT_CHANNELS = args.out_channels

def preprocess_inputs(x):
    return preprocess_input(x, mode=args.preprocessing_function)

if __name__ == '__main__':
    t0 = timeit.default_timer()

    weights = [os.path.join(args.models_dir, m) for m in args.models]
    models = []
    for w in weights:
        model = make_model(args.network, (None, None, 3))
        print("Building model {} from weights {} ".format(args.network, w))
        model.load_weights(w)
        models.append(model)
    os.makedirs(test_pred, exist_ok=True)
    print('Predicting test')
    for d in tqdm(listdir(test_folder)):
        if not path.isdir(path.join(test_folder, d)):
            continue
        final_mask = None
        for scale in range(1):
            fid = d
            img = cv2.imread(path.join(test_folder, fid, 'images', '{0}.png'.format(fid)), cv2.IMREAD_COLOR)[...,::-1]

            if final_mask is None:
                final_mask = np.zeros((img.shape[0], img.shape[1], OUT_CHANNELS))
            if scale == 1:
                img = cv2.resize(img, None, fx=0.75, fy=0.75, interpolation=cv2.INTER_AREA)
            elif scale == 2:
                img = cv2.resize(img, None, fx=1.25, fy=1.25, interpolation=cv2.INTER_CUBIC)

            x0 = 16
            y0 = 16
            x1 = 16
            y1 = 16
            if (img.shape[1] % 32) != 0:
                x0 = int((32 - img.shape[1] % 32) / 2)
                x1 = (32 - img.shape[1] % 32) - x0
                x0 += 16
                x1 += 16
            if (img.shape[0] % 32) != 0:
                y0 = int((32 - img.shape[0] % 32) / 2)
                y1 = (32 - img.shape[0] % 32) - y0
                y0 += 16
                y1 += 16
            img0 = np.pad(img, ((y0, y1), (x0, x1), (0, 0)), 'symmetric')

            # inp0 = []
            # inp1 = []
            # for flip in range(2):
            #     for rot in range(4):
            #         if flip > 0:
            #             img = img0[::-1, ...]
            #         else:
            #             img = img0
            #         if rot % 2 == 0:
            #             inp0.append(np.rot90(img, k=rot))
            #         else:
            #             inp1.append(np.rot90(img, k=rot))
            #
            # inp0 = np.asarray(inp0)
            # inp0 = preprocess_inputs(np.array(inp0, "float32"))
            # inp1 = np.asarray(inp1)
            # inp1 = preprocess_inputs(np.array(inp1, "float32"))

            # mask = np.zeros((img0.shape[0], img0.shape[1], OUT_CHANNELS))

            # for model in models:
            #     pred0 = model.predict(inp0, batch_size=1)
            #     pred1 = model.predict(inp1, batch_size=1)
            #     j = -1
            #     for flip in range(2):
            #         for rot in range(4):
            #             j += 1
            #             if rot % 2 == 0:
            #                 pr = np.rot90(pred0[int(j / 2)], k=(4 - rot))
            #             else:
            #                 pr = np.rot90(pred1[int(j / 2)], k=(4 - rot))
            #             if flip > 0:
            #                 pr = pr[::-1, ...]
            #             mask += pr  # [..., :2]

            mask = np.zeros((img0.shape[0], img0.shape[1], OUT_CHANNELS))
            for model in models:
                inp = preprocess_inputs(np.array([img0], "float32"))
                pred = model.predict(inp)
                mask += pred[0]

            mask /= (len(models))
            mask = mask[y0:mask.shape[0] - y1, x0:mask.shape[1] - x1, ...]
            if scale > 0:
                mask = cv2.resize(mask, (final_mask.shape[1], final_mask.shape[0]))
            final_mask += mask
        final_mask /= 1
        if OUT_CHANNELS == 2:
            final_mask = np.concatenate([final_mask, np.zeros_like(final_mask)[..., 0:1]], axis=-1)
        final_mask = final_mask * 255
        final_mask = final_mask.astype('uint8')
        cv2.imwrite(path.join(test_pred, '{0}.png'.format(fid)), final_mask, [cv2.IMWRITE_PNG_COMPRESSION, 9])

    elapsed = timeit.default_timer() - t0
    print('Time: {:.3f} min'.format(elapsed / 60))