[神经网络]一步一步使用Mobile-Net完成视觉识别（五）

import osimport cv2import numpy as npimport tensorflow as tfimport syssys.path.append("..")from utils import label_map_utilfrom utils import visualization_utils as vis_utilENERMY = 2 # 1 代表蓝色方，2 代表红色方 ，设置蓝色方为敌人DEBUG = FalseTHRE_VAL = 0.2PATH_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 = 2label_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()