import numpy as np
import tensorflow as tf
from object_detection.utils import ops as utils_ops
from PIL import Image



PERSON_CLASS = 1
SCORE_THRESHOLD = 0.5

def run_inference_for_single_image(image, sess):
    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[1], image.shape[2])
        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: image})

    # 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.int64)
    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


def open_graph():
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.compat.v1.GraphDef()
        with tf.compat.v2.io.gfile.GFile('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb', 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
    return detection_graph


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)


def get_person(image_path, sess):
    img = Image.open(image_path)
    image_np = load_image_into_numpy_array(img)
    image_np_expanded = np.expand_dims(image_np, axis=0)
    output_dict = run_inference_for_single_image(image_np_expanded, sess)

    persons_coordinates = []
    for i in range(len(output_dict["detection_boxes"])):
        score = output_dict["detection_scores"][i]
        classtype = output_dict["detection_classes"][i]
        if score > SCORE_THRESHOLD and classtype == PERSON_CLASS:
            persons_coordinates.append(output_dict["detection_boxes"][i])

    w, h = img.size
    for person_coordinate in persons_coordinates:
        cropped_img = img.crop((
            int(w * person_coordinate[1]),
            int(h * person_coordinate[0]),
            int(w * person_coordinate[3]),
            int(h * person_coordinate[2]),
        ))
        return cropped_img
    return None
numpy.expand_dims