Violence detecting in videos using Tensorflow Object Detection

이미 트레이닝 시킨 것을 가지고 비디오 영상으로 테스트 해보기

object_detection_ 사용

 

 

detection_video.py 수정

""" 
Sections of this code were taken from: 
https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb 
""" 
import numpy as np 

import os 
import six.moves.urllib as urllib 
import sys 
import tarfile 
import tensorflow as tf 
import zipfile 

from collections import defaultdict 
from io import StringIO 
from matplotlib import pyplot as plt 
from PIL import Image 

from utils import label_map_util 

from utils import visualization_utils as vis_util 

import cv2 

# Path to frozen detection graph. This is the actual model that is used 
# for the object detection. 
PATH_TO_CKPT = 'inference_graph_ISeeU/frozen_inference_graph.pb' 

# List of the strings that is used to add correct label for each box. 
PATH_TO_LABELS = os.path.join('data', 'label_map.pbtxt') 

NUM_CLASSES = 3 

sys.path.append("..") 


def detect_in_video(): 

    # VideoWriter is the responsible of creating a copy of the video 
    # used for the detections but with the detections overlays. Keep in 
    # mind the frame size has to be the same as original video. 
    out = cv2.VideoWriter('fight_out.avi', cv2.VideoWriter_fourcc( 
        'M', 'J', 'P', 'G'), 10, (1280, 720)) 

    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) 

    with detection_graph.as_default(): 
        with tf.Session(graph=detection_graph) as sess: 
            # Definite input and output Tensors for detection_graph 
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') 
            # 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 represent how level of confidence for each of the objects. 
            # 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') 
            num_detections = detection_graph.get_tensor_by_name( 
                'num_detections:0') 
            cap = cv2.VideoCapture('fight.avi') 

            while(cap.isOpened()): 
                # Read the frame 
                ret, frame = cap.read() 

                # Recolor the frame. By default, OpenCV uses BGR color space. 
                # This short blog post explains this better: 
                # https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/ 
                color_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 

                image_np_expanded = np.expand_dims(color_frame, axis=0) 

                # Actual detection. 
                (boxes, scores, classes, num) = sess.run( 
                    [detection_boxes, detection_scores, 
                        detection_classes, num_detections], 
                    feed_dict={image_tensor: image_np_expanded}) 

                # Visualization of the results of a detection. 
                # note: perform the detections using a higher threshold 
                vis_util.visualize_boxes_and_labels_on_image_array( 
                    color_frame, 
                    np.squeeze(boxes), 
                    np.squeeze(classes).astype(np.int32), 
                    np.squeeze(scores), 
                    category_index, 
                    use_normalized_coordinates=True, 
                    line_thickness=8, 
                    min_score_thresh=.20) 

                cv2.imshow('frame', color_frame) 
                output_rgb = cv2.cvtColor(color_frame, cv2.COLOR_RGB2BGR) 
                out.write(output_rgb) 

                if cv2.waitKey(1) & 0xFF == ord('q'): 
                    break 

            out.release() 
            cap.release() 
            cv2.destroyAllWindows() 


def main(): 
    detect_in_video() 


if __name__ == '__main__': 
    main() 

 

비디오를 넣었을 때 ready, low_level, high_level의 3개 class  탐지 가능

 

 

 

참고

https://github.com/juandes/pikachu-detection/blob/master/detection_video.py