import numpy as np
import os
import sys
import tensorflow as tf
from matplotlib import pyplot as plt
from PIL import Image
import cv2
import pytesseract

from custom_plate import allow_needed_values as anv 
from custom_plate import do_image_conversion as dic

get_ipython().magic('matplotlib inline')
sys.path.append("..")

from utils import label_map_util
from utils import visualization_utils as vis_util

MODEL_NAME = 'numplate'
PATH_TO_CKPT = MODEL_NAME + '/graph-200000/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')
NUM_CLASSES = 1


detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')


label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)


def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


PATH_TO_TEST_IMAGES_DIR = 'png_tesseract/test_ram'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(5, 6) ]
IMAGE_SIZE = (12, 8)
TEST_DHARUN=os.path.join('numplate')
count = 0


with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path) 
      image_np = load_image_into_numpy_array(image)
      image_np_expanded = np.expand_dims(image_np, axis=0)
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      ymin = boxes[0,0,0]
      xmin = boxes[0,0,1]
      ymax = boxes[0,0,2]
      xmax = boxes[0,0,3]
      (im_width, im_height) = image.size
      (xminn, xmaxx, yminn, ymaxx) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
      cropped_image = tf.image.crop_to_bounding_box(image_np, int(yminn), int(xminn),int(ymaxx - yminn), int(xmaxx - xminn))
      img_data = sess.run(cropped_image)
      count = 0
      filename = dic.yo_make_the_conversion(img_data, count)
      pytesseract.tesseract_cmd = '/home/tensorflow-cuda/dharun_custom/models/research/object_detection/tessdata/'
      text = pytesseract.image_to_string(Image.open(filename),lang=None) 
      print('CHARCTER RECOGNITION : ',anv.catch_rectify_plate_characters(text))
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=5)
      
      
      plt.figure(figsize=IMAGE_SIZE)
#      plt.imshow(img_data)
#      plt.show()