一、准备

数据集：coco

模型：目标检测常用的三个模型有：SSD、Faster R-CNN、YOLO

免去训练的过程，模型成品下载：github地址

环境：TensorFlow 1.14.0、openCV 4.1.1

二、检测

1、罗列类别名称

person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant

stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe

backpack
umbrella


handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle

wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
potted plant
bed

dining table


toilet

tv monitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator

book
clock
vase
scissors
teddy bear
hair drier
toothbrush

 2、下载模型

比如下载   faster_rcnn_inception_v2_coco_2018_01_28  解压到当前目录下

准备好待检测图片

import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf

# 加载coco数据集模型
model_path = "faster_rcnn_inception_v2_coco_2018_01_28"
frozen_pb_file = os.path.join(model_path, 'frozen_inference_graph.pb')

# 加载coco数据集分类
f = open("coco/classes.txt", "r")
class_names = f.readlines()

# model_path = ""
# frozen_pb_file = os.path.join(model_path, 'model.pb')


score_threshold = 0.3

img_file = 'pic/class.jpg'

# Read the graph.
with tf.gfile.FastGFile(frozen_pb_file, 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())


with tf.Session() as sess:
    # Restore session
    sess.graph.as_default()
    tf.import_graph_def(graph_def, name='')

    # for op in sess.graph.get_operations():
    #     print(op)

    # Read and preprocess an image.
    img_cv2 = cv2.imread(img_file)
    img_height, img_width, _ = img_cv2.shape

    img_in = cv2.resize(img_cv2, (300, 300))
    img_in = img_in[:, :, [2, 1, 0]]  # BGR2RGB

    # Run the model
    outputs = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
                    sess.graph.get_tensor_by_name('detection_scores:0'),
                    sess.graph.get_tensor_by_name('detection_boxes:0'),
                    sess.graph.get_tensor_by_name('detection_classes:0')],
                   feed_dict={
                       'image_tensor:0': img_in.reshape(1,
                                                        img_in.shape[0],
                                                        img_in.shape[1],
                                                        3)})

    # Visualize detected bounding boxes.
    num_detections = int(outputs[0][0])
    for i in range(num_detections):
        classId = int(outputs[3][0][i])
        score = float(outputs[1][0][i])
        bbox = [float(v) for v in outputs[2][0][i]]
        if score > score_threshold:
            x = bbox[1] * img_width
            y = bbox[0] * img_height
            right = bbox[3] * img_width
            bottom = bbox[2] * img_height
            # 标框
            cv2.rectangle(img_cv2,
                          (int(x), int(y)),
                          (int(right), int(bottom)),
                          (125, 255, 51),
                          thickness=3)
            # 文字"class_name, score"
            cv2.putText(img_cv2,
                        class_names[classId - 1][:-1] + "," + str("%.2f" % score),
                        (int(x), int(y)),
                        cv2.FONT_HERSHEY_DUPLEX, 3, (0, 0, 255), 3)
            print(str(classId) + ",class:" + class_names[classId - 1][:-1] + ",score:%.2f" % score)

plt.figure(figsize=(10, 8))
plt.imshow(img_cv2[:, :, ::-1])
plt.title("TensorFlow MobileNetV2-SSD")
plt.axis("off")
plt.show()

三、后期展望

使用新的数据训练自己的model，进行SSD或者Faster R-CNN模型的迁移学习，运用到更具体的场景中去。

四、参考文献

【1】TensorFlow 目标检测模型转换为 OpenCV DNN 可调用格式

【2】SSD模型的原理

【3】coco2017数据集80类别名称与id号的对应关系

最后

以上就是过时小蜜蜂为你收集整理的tensorflow+openCV进行目标检测一、准备二、检测三、后期展望四、参考文献的全部内容，希望文章能够帮你解决tensorflow+openCV进行目标检测一、准备二、检测三、后期展望四、参考文献所遇到的程序开发问题。

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