<gh_stars>0
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
Run a YOLO_v3 style detection model on test images.
"""

import colorsys
import os
from timeit import default_timer as timer

import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
import os, errno
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from keras.utils import multi_gpu_model
gpu_num=1

import sql #add
from io import StringIO #add
from io import BytesIO #add
import io, base64 #add

import json
from urllib.parse import urlencode
from urllib.request import Request, urlopen

class YOLO(object):
    def __init__(self):
        self.model_path = 'model_data/yolo.h5' # model path or trained weights path
        self.anchors_path = 'model_data/yolo_anchors.txt'
        self.classes_path = 'model_data/coco_classes.txt'
        self.score = 0.3
        self.iou = 0.45
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.model_image_size = (416, 416) # fixed size or (None, None), hw
        self.boxes, self.scores, self.classes = self.generate()
        self.counter_image = 1
        self.images_path = './images'
        self.frames_path = self.images_path + '/frames/'
        self.object_path = self.images_path + '/objects/'

        try:
            os.makedirs(self.images_path)
        except OSError as e:
            if e.errno != errno.EEXIST:
                raise

        try:
            os.makedirs(self.frames_path)
        except OSError as e:
            if e.errno != errno.EEXIST:
                raise

    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    def _get_anchors(self):
        anchors_path = os.path.expanduser(self.anchors_path)
        with open(anchors_path) as f:
            anchors = f.readline()
        anchors = [float(x) for x in anchors.split(',')]
        return np.array(anchors).reshape(-1, 2)

    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

        # Load model, or construct model and load weights.
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)
        is_tiny_version = num_anchors==6 # default setting
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
        else:
            assert self.yolo_model.layers[-1].output_shape[-1] == \
                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                'Mismatch between model and given anchor and class sizes'

        print('{} model, anchors, and classes loaded.'.format(model_path))

        # Generate colors for drawing bounding boxes.
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(
            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
                self.colors))
        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        np.random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        if gpu_num>=2:
            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=gpu_num)
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                len(self.class_names), self.input_image_shape,
                score_threshold=self.score, iou_threshold=self.iou)
        return boxes, scores, classes

    def detect_image(self, image):
        start = timer()

        if self.model_image_size != (None, None):
            assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
            assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
        else:
            new_image_size = (image.width - (image.width % 32),
                              image.height - (image.height % 32))
            boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype='float32')

        print(image_data.shape)
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

        out_boxes, out_scores, out_classes = self.sess.run(
            [self.boxes, self.scores, self.classes],
            feed_dict={
                self.yolo_model.input: image_data,
                self.input_image_shape: [image.size[1], image.size[0]],
                K.learning_phase(): 0
            })

        print('Found {} boxes for {}'.format(len(out_boxes), 'img'))

        font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                    size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
        thickness = (image.size[0] + image.size[1]) // 300

        history = open('history_object_detection.txt', 'a')
        db = 'test.db' 

        outputBuffer = BytesIO()
        image.save(outputBuffer, format='JPEG')
        imageBase64Data = outputBuffer.getvalue()
        data = base64.b64encode(imageBase64Data)
        outputBuffer.close
        sql.add_record(db, data)

        #sql.extr_record(db, './output', 1)

        for i, c in reversed(list(enumerate(out_classes))):
            predicted_class = self.class_names[c]
            box = out_boxes[i]
            score = out_scores[i]

            label = '{} {:.2f}'.format(predicted_class, score)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)

            top, left, bottom, right = box
            top = max(0, np.floor(top + 0.5).astype('int32'))
            left = max(0, np.floor(left + 0.5).astype('int32'))
            bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
            right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
            print(label, (left, top), (right, bottom))

            if top - label_size[1] >= 0:
                text_origin = np.array([left, top - label_size[1]])
            else:
                text_origin = np.array([left, top + 1])

            # Закоментировать что бы убрать отрисовку прямоугольников
            # My kingdom for a good redistributable image drawing library.
            for i in range(thickness):
                draw.rectangle(
                    [left + i, top + i, right - i, bottom - i],
                    outline=self.colors[c])
            draw.rectangle(
                [tuple(text_origin), tuple(text_origin + label_size)],
                fill=self.colors[c])
            draw.text(text_origin, label, fill=(0, 0, 0), font=font)
            del draw

            class_object = label.split(' ')[0]

            def save_object():
                area = (left, top, right, bottom)
                cropped_image = image.crop(area)
                cropped_image.save(self.object_path + class_object + '/frame_' + str(self.counter_image) + '_' + label + '.jpeg')

                outputBuffer = BytesIO()
                cropped_image.save(outputBuffer, format='JPEG')
                imageBase64Data = outputBuffer.getvalue()
                data = base64.b64encode(imageBase64Data)
                outputBuffer.close
                edit_score = '{:.2f}'.format(score)

                face_id = '';
                face_accuracy = 0;
                

                # Face ID request
                if label.count('person'):
                    url = 'https://api.evision.tech/predict/2.0' # Set destination URL here
                    post_fields = {
                        "key": "",
                        "minAccuracy": 30,
                        "type": "check",
                        "headers": {
                            'Content-Type': "application/json"
                        },
                        "image": "data:image/jpeg;base64," + str(data)[2:]
                    }

                    request = Request(url, urlencode(post_fields).encode())
                    resultRequest = json.loads(urlopen(request).read().decode())
                    print(resultRequest)

                    sql.add_record_class(db, predicted_class)

                    if ( resultRequest and resultRequest['success'] 
                        and resultRequest['data'] and len(resultRequest['data']) 
                        and resultRequest['data'][0] and resultRequest['data'][0]['id']
                        and resultRequest['data'][0]['accuracy'] ):
                        face_id = resultRequest['data'][0]['id']
                        face_accuracy = resultRequest['data'][0]['accuracy']

                        # print('face_accuracy = ', face_accuracy)
                        sql.add_record_child(db, predicted_class, edit_score, data, face_id, face_accuracy)
                    



                #sql.child_extr_record(db, './output', 1)

                history.write('Class: ' + class_object + ' Frame: ' + str(self.counter_image) + ' Probability: ' + label + '\r\n')

            if not os.path.exists(self.object_path + class_object):
                try:
                    os.makedirs(self.object_path + class_object)
                    save_object()
                except OSError as e:
                    if e.errno != errno.EEXIST:
                        raise
            else:
                save_object()

        history.write('\r\n')
        history.close()
        end = timer()
        print(end - start)
        image.save(self.frames_path + str(self.counter_image) + '.jpeg')

        self.counter_image+=1

        return image

    def close_session(self):
        self.sess.close()


def detect_video(yolo, video_path, output_path=""):
    import cv2
    print('video_path ', video_path)
    if video_path == '0':
        vid = cv2.VideoCapture(0)
    else: 
        vid = cv2.VideoCapture(video_path)
    if not vid.isOpened():
        raise IOError("Couldn't open webcam or video")
    video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))
    video_fps       = vid.get(cv2.CAP_PROP_FPS)
    video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
                        int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    isOutput = True if output_path != "" else False
    if isOutput:
        print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
        out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
    accum_time = 0
    curr_fps = 0
    fps = "FPS: ??"
    prev_time = timer()
    while True:
        try:
            return_value, frame = vid.read()
            image = Image.fromarray(frame)
            image = yolo.detect_image(image)
            result = np.asarray(image)
            curr_time = timer()
            exec_time = curr_time - prev_time
            prev_time = curr_time
            accum_time = accum_time + exec_time
            curr_fps = curr_fps + 1
            if accum_time > 1:
                accum_time = accum_time - 1
                fps = "FPS: " + str(curr_fps)
                curr_fps = 0
            cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                        fontScale=0.50, color=(255, 0, 0), thickness=2)
            cv2.namedWindow("result", cv2.WINDOW_NORMAL)
            cv2.imshow("result", result)
            if isOutput:
                out.write(result)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
        except:
            print('Error get frame cv2')
            continue
    yolo.close_session()


def detect_img(yolo):
    while True:
        img = input('Input image filename:')
        try:
            image = Image.open(img)
        except:
            print('Open Error! Try again!')
            continue
        else:
            r_image = yolo.detect_image(image)
            r_image.show()
    yolo.close_session()



if __name__ == '__main__':
    detect_img(YOLO())

# Запрос в бд изображения
# result = child_extr_record(db, './img_from_bd', 8)

# # print(result)

# if result != None:
#     for record in result:
#         rowid, ablob, PERCENT, CLASSES_ID_CLASS, *_ = record
#         # print(ablob)
#         # child_output_file.write(base64.b64decode(ablob))
#         if CLASSES_ID_CLASS == 'person':
#             from urllib.parse import urlencode
#             from urllib.request import Request, urlopen

#             url = 'https://api.evision.tech/predict/2.0' # Set destination URL here
#             post_fields = {
#                 "key": "",
#                 "minAccuracy": 1,
#                 "type": "check",
#                 "headers": {
#                     'Content-Type': "application/json"
#                 },
#                 "image": "data:image/jpeg;base64," + str(ablob)[2:]
#             }

#             # print(str(ablob)[2:100])
#             print(post_fields['image'][0:100])

#             # print(post_fields)

#             request = Request(url, urlencode(post_fields).encode())
#             json = urlopen(request).read().decode()
#             print(json)