# Import packages
import os
import sys

import cv2
import numpy as np
import tensorflow as tf

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = 'model'

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'frozen_inference_graph.pb')

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH, 'data', 'labelmap.pbtxt')

# Number of classes the object detector can identify
NUM_CLASSES = 1

# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
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)

# Load the Tensorflow model into memory.
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='')

    sess = tf.Session(graph=detection_graph)

# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')


# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value

def get_card_from_image(image):
    """
    :param image: image = cv2.imread(image_path)
    :return: {
        "success": Boolean,
        "output_image": OpenCv Image object,
        "reason": Reason for failure in case success is False
    }
    """

    image_expanded = np.expand_dims(image, axis=0)

    # Perform the actual detection by running the model with the image as input
    (boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],
                                             feed_dict={image_tensor: image_expanded})

    # Draw the results of the detection (aka 'visulaize the results')
    image1, array_coord = vis_util.visualize_boxes_and_labels_on_image_array(image,
                                                                             np.squeeze(boxes),
                                                                             np.squeeze(classes).astype(np.int32),
                                                                             np.squeeze(scores),
                                                                             category_index,
                                                                             use_normalized_coordinates=True,
                                                                             line_thickness=3,
                                                                             min_score_thresh=0.60)

    ymin, xmin, ymax, xmax = array_coord
    shape = np.shape(image1)
    im_width, im_height = shape[1], shape[0]
    (left, right, top, bottom) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)

    if ymin == xmax == xmin == ymax == 0:
        return {
            'success': False,
            'output_image': image1,
            'reason': 'Unable to detect image in card'
        }
    else:
        cropped_image = image[int(top):int(bottom), int(left):int(right)]
        return {
            'success': True,
            'output_image': cropped_image,
        }


if __name__ == '__main__':
    test_image_filepath = '/Users/syedhassanashraf/Documents/python/extractor/test/image1.png'
    image = cv2.imread(test_image_filepath)
    cropped_image_response = get_card_from_image(image)
    if cropped_image_response['success']:
        cv2.imshow("cropped_card", cropped_image_response['output_image'])
        cv2.waitKey(0)
