import base64

import flask
from flask import Flask, jsonify
import numpy as np
import io
from PIL import Image, ImageDraw
import tensorflow as tf
import ops as utils_ops
import visualization_utils as vis_util

from serve import serve_unet_model
from serve import serve_rcnn_model

app = Flask(__name__)


def load_unet_model():
    global tflite_interpreter_c, height_c, width_c, input_details_c, output_details_c
    tflite_interpreter_c, height_c, width_c, input_details_c, output_details_c = serve_unet_model()


def load_rcnn_model():
    global detection_graph
    detection_graph = serve_rcnn_model()


load_unet_model()
load_rcnn_model()


def prepare_img(image, type):
    if type == "detect":
        return image.resize((width, height))
    elif type == "segment":
        return image.resize((width_c, height_c))


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)


@app.route("/bridge/crack", methods=["POST"])
def segment():
    if flask.request.method == "POST":
        if flask.request.files.get("image"):
            # read the image in PIL format
            img = prepare_img(Image.open(flask.request.files["image"]), "segment")

            input_data = np.expand_dims(img, axis=0)
            input_data = np.float32(input_data) / 255.0
            tflite_interpreter_c.set_tensor(input_details_c[0]['index'], input_data)
            tflite_interpreter_c.invoke()
            result = tflite_interpreter_c.get_tensor(output_details_c[0]['index'])
            result = result > 0.5
            result = result * 255
            mask = np.squeeze(result)
            bg = np.asarray(img).copy()
            is_crack = False
            for i in range(len(mask)):
                for j in range(len(mask[i])):
                    if mask[i][j] > 0:
                        bg[i][j][0] = 0
                        bg[i][j][1] = 0
                        bg[i][j][2] = 255
                        is_crack = True

            img = Image.fromarray(bg.astype("uint8"))
            raw_bytes = io.BytesIO()
            img.save(raw_bytes, "JPEG")
            raw_bytes.seek(0)
            img_byte = raw_bytes.getvalue()
            img_str = base64.b64encode(img_byte)
            data = {
                "result": is_crack,
                "img": img_str.decode('utf-8')
            }
            return jsonify(data)
        else:
            return "Could not find image"
    return "Please use POST method"


def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.compat.v1.Session() as sess:
            # Get handles to input and output tensors
            ops = tf.compat.v1.get_default_graph().get_operations()
            all_tensor_names = {
                output.name for op in ops for output in op.outputs}
            tensor_dict = {}
            for key in [
                'num_detections', 'detection_boxes', 'detection_scores',
                'detection_classes', 'detection_masks'
            ]:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.compat.v1.get_default_graph().get_tensor_by_name(
                        tensor_name)
            if 'detection_masks' in tensor_dict:
                # The following processing is only for single image
                detection_boxes = tf.squeeze(
                    tensor_dict['detection_boxes'], [0])
                detection_masks = tf.squeeze(
                    tensor_dict['detection_masks'], [0])
                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image
                # size.
                real_num_detection = tf.cast(
                    tensor_dict['num_detections'][0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [
                    real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                    real_num_detection, -1, -1])
                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                    detection_masks, detection_boxes, image.shape[0], image.shape[1])
                detection_masks_reframed = tf.cast(
                    tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                # Follow the convention by adding back the batch dimension
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name('image_tensor:0')

            # Run inference
            output_dict = sess.run(tensor_dict,
                                   feed_dict={image_tensor: np.expand_dims(image, 0)})

            # all outputs are float32 numpy arrays, so convert types as appropriate
            output_dict['num_detections'] = int(
                output_dict['num_detections'][0])
            output_dict['detection_classes'] = output_dict[
                'detection_classes'][0].astype(np.uint8)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'][0]
    return output_dict


if __name__ == "__main__":
    app.run()