了解caffe，使用caffe，做个小例子熟悉caffe。

介绍

caffe Github： https://github.com/BVLC/caffe

caffe Tutorial :https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_498

why caffe： only for vision； very simple。caffe不用懂程序都能写出模型。

拥有一个docker环境：https://github.com/BVLC/caffe/tree/master/docker

caffe mnist Tutorial :https://caffe.berkeleyvision.org/gathered/examples/mnist.html

微信二维码opencv模型文件：https://github.com/WeChatCV/opencv_3rdparty
微信二维码opencv源码：https://github.com/opencv/opencv_contrib/blob/master/modules/wechat_qrcode/src/wechat_qrcode.cpp#L156
微信二维码技术介绍：https://zhuanlan.zhihu.com/p/348412200

docker环境

cpu:

cd ~
mkdir caffetest
docker run -ti -v ~/caffetest/:/app bvlc/caffe:cpu bash
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MNIST 数据（废弃）

代码下载下来，宿主机执行：

cd $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
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我没编译安装caffe，执行create_mnist.sh报错，lmdb数据格式何方神圣？：

caffe直接处理的数据分为两种格式： lmdb和leveldb，两者关系： 它们都是键/值对嵌入式数据库管理系统编程库
虽然lmdb的内存消耗是leveldb的1.1倍，但是lmdb的处理速度比leveldb快10%到15%，另外lmdb允许多种训练模型同时读取同一组数据集。
lmdb取代了leveldb成为了caffe默认的数据集生成格式。

docker 内的Linux发行版查看cat /etc/issue，

Ubuntu 16.04.4 LTS \n \l
1

dockerfile:https://github.com/BVLC/caffe/blob/9b891540183ddc834a02b2bd81b31afae71b2153/docker/cpu/Dockerfile
ubuntu安装教程：http://caffe.berkeleyvision.org/install_apt.html

ubuntu的update太慢了，这条路以后再走。

微信二维码

微信二维码

我想在python里面调用起二维码检测的代码，不用识别二维码，只进行目标检测。

在线可视化prototxt模型：http://ethereon.github.io/netscope/#/editor

看源码：

输入图像的处理需要：
（1）太小就不要了，他这是为了二维码解码，这步不管。
（2）灰度化图片。
（3）归一化图片。
（4）得输出。

python代码试试

import math

import cv2
import numpy as np

cvNet = cv2.dnn.readNetFromCaffe('detect.prototxt', 'detect.caffemodel')
image_path = r'C:\Users\dong.xie\Desktop\workcode\kevintest\google_crawler_for_linux\src\生活 二维码_1\img000001.jpg'
img2 = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), 1)  # img是矩阵

img = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

img_w = img.shape[1]
img_h = img.shape[0]
minInputSize = 400
resizeRatio = math.sqrt(img_w * img_h * 1.0 / (minInputSize * minInputSize))
target_width = int(img_w / resizeRatio)
target_height = int(img_h / resizeRatio)

input = cv2.resize(img, (target_width, target_height), interpolation=cv2.INTER_CUBIC)

blob = cv2.dnn.blobFromImage(input, scalefactor=1.0 / 255, size=(input.shape[1], input.shape[0]), mean=(0, 0, 0),
                             swapRB=False, crop=False)

cvNet.setInput(blob, name="data")
prob = cvNet.forward()
print(prob.shape)


#  define CLIP(x, x1, x2) max(x1, min(x, x2))
def CLIP(x, x1, x2):
    return int(max(x1, min(x, x2)))


#  theshape is (1, 1, 100, 7) = > (batch, channel, count, dim)
for row in range(0, prob.shape[2]):
    prob_score = prob[0][0][row]
    # prob_score[0] is not used.
    # prob_score[1]==1 stands for qrcode
    # add a safe score threshold due to https://github.com/opencv/opencv_contrib/issues/2877
    # prob_score[2] is the probability of the qrcode, which is not used.
    if prob_score[1] == 1 and prob_score[2] > 1E-5:
        x0 = CLIP(prob_score[3] * img_w, 0.0, img_w - 1.0)
        y0 = CLIP(prob_score[4] * img_h, 0.0, img_h - 1.0)
        x1 = CLIP(prob_score[5] * img_w, 0.0, img_w - 1.0)
        y1 = CLIP(prob_score[6] * img_h, 0.0, img_h - 1.0)

        cv2.rectangle(img2, (x0, y0), (x1, y1), (0, 0, 255), 2)

cv2.imshow("1", img2)
cv2.waitKey()
cv2.destroyAllWindows()

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还是挺成功的：

用于造yolov5的目标检测数据

# pip install opencv-contrib-python
import math
import os
import traceback

import cv2
import numpy as np

dataFile = r'''C:\Users\dong.xie\Desktop\12312'''
dst = r'E:\detection\02二维码\images'
dst1 = r'E:\detection\02二维码\labels'
cnt = 3000


def listPathAllfiles(dirname):
    result = []
    for maindir, subdir, file_name_list in os.walk(dirname):
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)
            result.append(apath)
    return result


if __name__ == '__main__':
    files = listPathAllfiles(dataFile)

    cvNet = cv2.dnn.readNetFromCaffe('detect.prototxt', 'detect.caffemodel')

    for file in files:
        try:
            if file.endswith(".jpg") or file.endswith(".png"):
                img2 = cv2.imdecode(np.fromfile(file, dtype=np.uint8), 1)  # img是矩阵

                img = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

                img_w = img.shape[1]
                img_h = img.shape[0]
                minInputSize = 400
                resizeRatio = math.sqrt(img_w * img_h * 1.0 / (minInputSize * minInputSize))
                target_width = int(img_w / resizeRatio)
                target_height = int(img_h / resizeRatio)

                input = cv2.resize(img, (target_width, target_height), interpolation=cv2.INTER_CUBIC)

                blob = cv2.dnn.blobFromImage(input, scalefactor=1.0 / 255, size=(input.shape[1], input.shape[0]),
                                             mean=(0, 0, 0),
                                             swapRB=False, crop=False)

                cvNet.setInput(blob, name="data")
                prob = cvNet.forward()
                # print(prob.shape)


                #  define CLIP(x, x1, x2) max(x1, min(x, x2))
                def CLIP(x, x1, x2):
                    return int(max(x1, min(x, x2)))


                resDet = []
                #  theshape is (1, 1, 100, 7) = > (batch, channel, count, dim)
                for row in range(0, prob.shape[2]):
                    prob_score = prob[0][0][row]
                    # prob_score[0] is not used.
                    # prob_score[1]==1 stands for qrcode
                    # add a safe score threshold due to https://github.com/opencv/opencv_contrib/issues/2877
                    # prob_score[2] is the probability of the qrcode, which is not used.
                    if prob_score[1] == 1 and prob_score[2] > 1E-5:
                        x0 = CLIP(prob_score[3] * img_w, 0.0, img_w - 1.0)
                        y0 = CLIP(prob_score[4] * img_h, 0.0, img_h - 1.0)
                        x1 = CLIP(prob_score[5] * img_w, 0.0, img_w - 1.0)
                        y1 = CLIP(prob_score[6] * img_h, 0.0, img_h - 1.0)

                        resDet.append([x0, y0, x1, y1])

                if len(resDet) >= 1:
                    cv2.imencode('.jpg', img2)[1].tofile(os.path.join(dst, "qrcode_" + str(cnt).zfill(6) + ".jpg"))
                    txtfile = os.path.join(dst1, "qrcode_" + str(cnt).zfill(6) + ".txt")
                    res = []
                    for x0, y0, x1, y1 in resDet:
                        x = round((x0 + x1) / 2 / img.shape[1], 6)
                        y = round((y0 + y1) / 2 / img.shape[0], 6)
                        w = round((x1 - x0) / img.shape[1], 6)
                        h = round((y1 - y0) / img.shape[0], 6)
                        res.append(" ".join(["1", str(x), str(y), str(w), str(h)]))
                    open(txtfile, "w").write("\n".join(res))

                    cnt += 1
        except:
            traceback.print_exc()

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