[神经网络]一步一步使用Mobile-Net完成视觉识别（五）

import os
import cv2
import numpy as np
import tensorflow as tf
import sys
sys.path.append("..")
from utils import label_map_util
from utils import visualization_utils as vis_util

ENERMY = 2 # 1 代表蓝色方，2 代表红色方 ，设置蓝色方为敌人
DEBUG = False
THRE_VAL = 0.2

PATH_TO_CKPT =‘/home/xueaoru/models/research/inference_graph_v2/frozen_inference_graph.pb‘
PATH_TO_LABELS = ‘/home/xueaoru/models/research/object_detection/car_label_map.pbtxt‘
NUM_CLASSES = 2
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)
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)
image_tensor = detection_graph.get_tensor_by_name(‘image_tensor:0‘)
detection_boxes = detection_graph.get_tensor_by_name(‘detection_boxes:0‘)
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‘)

def video_test():
    #cap = cv2.VideoCapture(1)
    cap = cv2.VideoCapture("/home/xueaoru/下载/RoboMaster2.mp4")
    while(1):
        time = cv2.getTickCount()
        ret, image = cap.read()
        if ret!= True:
            break
        image_expanded = np.expand_dims(image, axis=0)#[1,w,h,3]

        (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: image_expanded})
        #print(np.squeeze(classes).astype(np.int32))
        #print(np.squeeze(scores))
        #print(np.squeeze(boxes))
        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=8,
        min_score_thresh=0.4)

        cv2.imshow(‘Object detector‘, image)
        key = cv2.waitKey(1)&0xff
        time = cv2.getTickCount() - time
        print("处理时间："+str(time*1000/cv2.getTickFrequency()))
        if key ==27:
            break
    cv2.destroyAllWindows()
def pic_test():
    image = cv2.imread("/home/xueaoru/models/research/images/image12.jpg")
    image_expanded = np.expand_dims(image, axis=0)  # [1,w,h,3]

    (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: image_expanded})
    
    if DEBUG:
        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=8,
        min_score_thresh=0.80)
    else:
        score = np.squeeze(scores)
        max_index = np.argmax(score)
        score = score[max_index]
        detected_class = np.squeeze(classes).astype(np.int32)[max_index]
        if score > THRE_VAL and detected_class == ENERMY:
            box = np.squeeze(boxes)[max_index]#(ymin,xmin,ymax,xmax)
            h,w,_ = image.shape
            min_point = (int(box[1]*w),int(box[0]*h))
            max_point = (int(box[3]*w),int(box[2]*h))
            cv2.rectangle(image,min_point,max_point,(0,255,255),2)


    
    cv2.imshow(‘Object detector‘, image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
video_test()