anaconda配置tensorflow Python实现一个简单的目标检测 以及

首先在anaconda配置tensorflow环境进行目标检测

打开anaconda prompt输入指令conda install tensorflow下载

输入conda list进行检测列表出现tensorflow相关的安装项目即可

 然后进行目标检测

import sys
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras import datasets, layers, models

# 读取图片
img = cv2.imread('D:/vision/1.jpg')
# 按比例缩放图片
newHeight = 100
newWidth = int(img.shape[1] * 100/ img.shape[0])
img = cv2.resize(img, (newWidth, newHeight))
# 创建选择性搜索分割对象
ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
# 设置输入图像，我们将运行分割
ss.setbaseImage(img)
# 快速但低召回选择性搜索方法
ss.switchToSelectiveSearchFast()
# 高召回但慢选择性搜索方法
ss.switchToSelectiveSearchQuality()

# 运行选择性搜索分割输入图像
rects = ss.process()
# print(rects)
print( 'Total Number of Region Proposals: {}'.format( len( rects ) ) )
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']
# 加载创建完全相同的模型，包括其权重和优化程序
#loaded_model = tf.keras.models.load_model('D:/vision/water/1.jpg')
while True:
# 创建原始图像的副本
	new_img = img.copy()
	# print(new_img)
	region_score = []
	max_rect = 0
	max_name = ""
	max_score = 0
	# 重复所有的区域建议
	for i, rect in enumerate( rects ):
		x, y, w, h = rect # 预测框的左上角坐标(x,y)以及框的宽w,高h
		pre_img = new_img[y:y+h,x:x+w]
		pre_img = cv2.resize(pre_img,(32,32))
		pre_img = (np.expand_dims(pre_img,0))
		# 输入的图片维度为(1,32,32,3)
		#pred_arr = loaded_model.predict(pre_img)
		# 预测标签
		#pre_label = np.argmax(pred_arr[0])
		# 预测得分
		#score = np.max(pred_arr[0])
		# 预测类名
		#class_name = class_names[pre_label]
		#if score > max_score:
		#	max_rect = rect
		#	max_name = class_name
		#	max_score = score
	#print([max_rect,max_name,max_score])
	#x,y,w,h = max_rect
	# cv2.rectangle(new_img, (x, y), (x + w, y + h), (0, 255, 0), 1, cv2.LINE_AA )
	cv2.rectangle(new_img, (x, y), (x + w, y + h), (0, 255, 0), 2, cv2.LINE_AA)
	font = cv2.FONT_HERSHEY_SIMPLEX
	text = max_name+" "+str(max_score*100)[0:4]+"%"
	cv2.putText(new_img, text, (x, y-5), font, 0.5, (0,0,255), 2)
	# 显示输出
	cv2.imshow("Output", new_img)
	# 等待按键输入
	k = cv2.waitKey( 0 ) & 0xFF
	# q键
	if k == 113:
		break
# 关闭所有窗口
cv2.destroyAllWindows()