从指定路径读取图像文件、利用OpenCV进行图像处理，以及使用Caffe框架进行深度学习预测的过程。
下面是程序的主要步骤和对应的实现代码总结：

1. 导入必要的工具包和模型

程序开始先导入需要的库os、numpy、cv2，同时导入utils_paths模块，后者用于处理图像路径。接着，读取Caffe模型和配置文件，这些文件提供了使用预训练深度学习模型进行图像分类的基础。

import utils_paths
import numpy as np
import cv2

net = cv2.dnn.readNetFromCaffe("bvlc_googlenet.prototxt", "bvlc_googlenet.caffemodel")

2. 读取图像文件

使用utils_paths.list_images函数遍历指定目录，获取所有图像文件的路径。

imagePaths = sorted(list(utils_paths.list_images("images/")))

3. 图像预处理

选择路径列表中的第一个图像进行读取，调整其大小以符合模型输入需求，并通过cv2.dnn.blobFromImage创建适合Caffe模型的输入blob。

image = cv2.imread(imagePaths[0])
resized = cv2.resize(image, (224, 224))
blob = cv2.dnn.blobFromImage(resized, 1, (224, 224), (104, 117, 123))

4. 模型预测和结果展示

设定模型输入，执行前向传播获取预测结果，找出概率最高的类别，并在图像上显示预测标签和概率。

net.setInput(blob)
preds = net.forward()
idx = np.argsort(preds[0])[::-1][0]
text = "Label: {}, {:.2f}%".format(classes[idx], preds[0][idx] * 100)
cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Image", image)
cv2.waitKey(0)

5. 批量图像处理

对多个图像执行上述步骤，生成多图像的输入blob，并对每个图像执行预测，展示结果。

images = []
for p in imagePaths[1:]:
    image = cv2.imread(p)
    image = cv2.resize(image, (224, 224))
    images.append(image)

blob = cv2.dnn.blobFromImages(images, 1, (224, 224), (104, 117, 123))
net.setInput(blob)
preds = net.forward()

for (i, p) in enumerate(imagePaths[1:]):
    image = cv2.imread(p)
    idx = np.argsort(preds[i])[::-1][0]
    text = "Label: {}, {:.2f}%".format(classes[idx], preds[i][idx] * 100)
    cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
    cv2.imshow("Image", image)
    cv2.waitKey(0)

完整代码

utils_paths.py

import os


image_types = (".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff")


def list_images(basePath, contains=None):
    # return the set of files that are valid
    return list_files(basePath, validExts=image_types, contains=contains)


def list_files(basePath, validExts=None, contains=None):
    # loop over the directory structure
    for (rootDir, dirNames, filenames) in os.walk(basePath):
        # loop over the filenames in the current directory
        for filename in filenames:
            # if the contains string is not none and the filename does not contain
            # the supplied string, then ignore the file
            if contains is not None and filename.find(contains) == -1:
                continue

            # determine the file extension of the current file
            ext = filename[filename.rfind("."):].lower()

            # check to see if the file is an image and should be processed
            if validExts is None or ext.endswith(validExts):
                # construct the path to the image and yield it
                imagePath = os.path.join(rootDir, filename)
                yield imagePath

blob_from_images.py

# 导入工具包
import utils_paths
import numpy as np
import cv2

# 标签文件处理
rows = open("synset_words.txt").read().strip().split("\n")
classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]

# Caffe所需配置文件
net = cv2.dnn.readNetFromCaffe("bvlc_googlenet.prototxt",
	"bvlc_googlenet.caffemodel")

# 图像路径
imagePaths = sorted(list(utils_paths.list_images("images/")))

# 图像数据预处理
image = cv2.imread(imagePaths[0])
resized = cv2.resize(image, (224, 224))
# image scalefactor size mean swapRB 
blob = cv2.dnn.blobFromImage(resized, 1, (224, 224), (104, 117, 123))
print("First Blob: {}".format(blob.shape))

# 得到预测结果
net.setInput(blob)
preds = net.forward()

# 排序，取分类可能性最大的
idx = np.argsort(preds[0])[::-1][0]
text = "Label: {}, {:.2f}%".format(classes[idx],
	preds[0][idx] * 100)
cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
	0.7, (0, 0, 255), 2)

# 显示
cv2.imshow("Image", image)
cv2.waitKey(0)

# Batch数据制作
images = []

# 方法一样，数据是一个batch
for p in imagePaths[1:]:
	image = cv2.imread(p)
	image = cv2.resize(image, (224, 224))
	images.append(image)

# blobFromImages函数，注意有s
blob = cv2.dnn.blobFromImages(images, 1, (224, 224), (104, 117, 123))
print("Second Blob: {}".format(blob.shape))

# 获取预测结果
net.setInput(blob)
preds = net.forward()
for (i, p) in enumerate(imagePaths[1:]):
	image = cv2.imread(p)
	idx = np.argsort(preds[i])[::-1][0]
	text = "Label: {}, {:.2f}%".format(classes[idx],
		preds[i][idx] * 100)
	cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
		0.7, (0, 0, 255), 2)
	cv2.imshow("Image", image)
	cv2.waitKey(0)

以下是后续代码的改进：

6. 异常处理和验证

在处理文件读取和图像处理时，加入异常处理可以避免在文件不存在或损坏时程序崩溃。

try:
    image = cv2.imread(imagePath)
    if image is None:
        raise ValueError("无法读取图像: {}".format(imagePath))
    resized = cv2.resize(image, (224, 224))
except Exception as e:
    print("处理图像时发生错误: ", e)

7. 性能优化

对于图像处理和预测，尤其是批量操作时，可以通过并行处理技术来加速这些操作。例如，使用Python的concurrent.futures模块进行并行读取和预处理图像。

from concurrent.futures import ThreadPoolExecutor

def process_image(path):
    image = cv2.imread(path)
    image = cv2.resize(image, (224, 224))
    return image

with ThreadPoolExecutor() as executor:
    images = list(executor.map(process_image, imagePaths))

8. 动态输入和命令行工具

将脚本转换为可接受命令行参数的形式，使其更灵活，能够通过命令行直接指定图片路径、模型文件等。

import argparse

parser = argparse.ArgumentParser(description='图像分类预测')
parser.add_argument('--image_dir', type=str, required=True, help='图像目录路径')
parser.add_argument('--model', type=str, required=True, help='模型文件路径')
args = parser.parse_args()

imagePaths = sorted(list(utils_paths.list_images(args.image_dir)))
net = cv2.dnn.readNetFromCaffe("bvlc_googlenet.prototxt", args.model)

9. GUI界面

为了使程序更友好，可以开发一个基于图形用户界面的应用，允许用户通过图形界面选择图像和观看结果，而不是仅限于命令行。

import tkinter as tk
from tkinter import filedialog

def load_image():
    path = filedialog.askopenfilename()
    return cv2.imread(path), path

root = tk.Tk()
load_button = tk.Button(root, text='加载图像', command=load_image)
load_button.pack()
root.mainloop()

初始代码 下载地址 dnn加载深度学习模型