SCF: 深度学习之目标检测-深蓝源码网

很多小伙伴，可能在使用SCF的时候，需要做一些深度学习的操作，但是SCF能跑起来深度学习么？这是个问题！那么，我们就尝试一下，看看如何让SCF跑起来深度学习！

有一张图：

需要用深度学习相关知识，识别出图的内容：

我们在本地已经训练好了模型，同时可以安装以下依赖：

pip3 install numpy scipy opencv-python pillow matplotlib h5py keras  -t /Users/dfounderliu/Documents/code/imageaidemo

pip3 install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.1/imageai-2.0.1-py3-none-any.whl -t /Users/dfounderliu/Documents/code/imageaidemo

注意一下，这个安装中-t后面的参数实际上是我们项目目录。

安装之后，可以下载两个训练好的模型文件：

https://mytest-1256773370.cos.ap-beijing.myqcloud.com/resnet50_coco_best_v2.0.1.h5

https://mytest-1256773370.cos.ap-beijing.myqcloud.com/resnet50_weights_tf_dim_ordering_tf_kernels.h5

这两个模型文件下载好了之后，我们可以实现代码：

# -*- coding: utf-8 -*-
from imageai.Prediction import ImagePrediction
from imageai.Detection import ObjectDetection
import os
import base64
import json
import hashlib
import PIL.Image as image
import imghdr


class ImageHandle:

    # 等比例压缩图片
    def resizeImg(self, **args):
        # try:
        args_key = {'ori_img': '', 'dst_img': '', 'dst_w': '', 'dst_h': '', 'save_q': 75}
        arg = {}
        for key in args_key:
            if key in args:
                arg[key] = args[key]

        im = image.open(arg['ori_img'])
        ori_w, ori_h = im.size
        widthRatio = heightRatio = None
        ratio = 1

        if (ori_w and ori_w > arg['dst_w']) or (ori_h and ori_h > arg['dst_h']):
            if arg['dst_w'] and ori_w > arg['dst_w']:
                widthRatio = float(arg['dst_w']) / ori_w  # 正确获取小数的方式
            if arg['dst_h'] and ori_h > arg['dst_h']:
                heightRatio = float(arg['dst_h']) / ori_h

            if widthRatio and heightRatio:
                if widthRatio < heightRatio:
                    ratio = widthRatio
                else:
                    ratio = heightRatio

            if widthRatio and not heightRatio:
                ratio = widthRatio
            if heightRatio and not widthRatio:
                ratio = heightRatio

            newWidth = int(ori_w * ratio)
            newHeight = int(ori_h * ratio)
        else:
            newWidth = ori_w
            newHeight = ori_h
        im.resize((newWidth, newHeight), image.ANTIALIAS).save(arg['dst_img'], quality=arg['save_q'])

        '''
        image.ANTIALIAS还有如下值：
        NEAREST: use nearest neighbour
        BILINEAR: linear interpolation in a 2x2 environment
        BICUBIC:cubic spline interpolation in a 4x4 environment
        ANTIALIAS:best down-sizing filter
        '''
        # except Exception as e:
        #     errorLogger(e)

    def getExecutionPath(self):
        return os.getcwd()

    def getMd5(self, strData):
        m = hashlib.md5()
        m.update(strData)
        return m.hexdigest()

    def imagePrediction(self, pathInputData):

        self.type = imghdr.what(pathInputData)
        if self.type == "jpeg":
            self.type = "jpg"
        if self.type not in ["jpg", "png"]:
            return None

        tempData = pathInputData[0:-4]

        pathData = tempData + ".jpg"

        zhuanhuanPic = tempData + "zh." + self.type

        # 目标图片大小
        dst_w = 400
        dst_h = 0
        # #保存的图片质量
        save_q = 40
        # 等比例压缩

        try:
            img = image.open(pathInputData)
            bg = image.new("RGB", img.size, (255, 255, 255))
            bg.paste(img, img)
            bg.save(pathInputData)
        except:
            pass

        self.resizeImg(ori_img=pathData, dst_img=zhuanhuanPic, dst_w=dst_w, dst_h=dst_h, save_q=save_q)

        prediction = ImagePrediction()
        prediction.setModelTypeAsResNet()
        prediction.setModelPath(os.path.join(self.getExecutionPath(), os.path.join(self.getExecutionPath(),
                                                                                   "tools/imageAI/resnet50_weights_tf_dim_ordering_tf_kernels.h5")))
        prediction.loadModel()

        predictions, probabilities = prediction.predictImage(os.path.join(self.getExecutionPath(), zhuanhuanPic),
                                                             result_count=5)
        result = []
        for eachPrediction, eachProbability in zip(predictions, probabilities):
            result.append(eachPrediction + " : " + eachProbability)
        return json.dumps(result)

    def objectDetection(self, pathInputData):

        self.type = imghdr.what(pathInputData)
        if self.type == "jpeg":
            self.type = "jpg"
        if self.type not in ["jpg", "png"]:
            return None

        tempData = pathInputData[0:-4]

        pathOutputData = tempData + "out." + self.type
        zhuanhuanPic = tempData + "zh." + self.type

        # 目标图片大小
        dst_w = 400
        dst_h = 0
        # #保存的图片质量
        save_q = 40
        # 等比例压缩

        try:
            img = image.open(pathInputData)
            bg = image.new("RGB", img.size, (255, 255, 255))
            bg.paste(img, img)
            bg.save(pathInputData)
        except:
            pass

        self.resizeImg(ori_img=pathInputData, dst_img=zhuanhuanPic, dst_w=dst_w, dst_h=dst_h, save_q=save_q)

        try:
            detector = ObjectDetection()
            detector.setModelTypeAsRetinaNet()
            detector.setModelPath(os.path.join(self.getExecutionPath(), "resnet50_coco_best_v2.0.1.h5"))
            detector.loadModel()
            detections = detector.detectObjectsFromImage(input_image=zhuanhuanPic, output_image_path=pathOutputData)

            with open(pathOutputData, "rb") as f:
                # b64encode是编码，b64decode是解码
                base64_data = base64.b64encode(f.read()).decode("utf-8")

            result = []
            for eachObject in detections:
                result.append(eachObject["name"] + " : " + eachObject["percentage_probability"])

            return json.dumps({
                "information": base64_data,
                "result": result})
        except Exception as e:
            return json.dumps({
                "information": str(e),
                "result": "error"})



imageData = ImageHandle()
result = imageData.objectDetection('1.png')
print(result)

这里面要注意修改你的目标图片名字以及刚才下载的文件存存放路径。

我们可以测试一个图像：

接下来，我们可以看一下文件压缩大小（北京区自带Tensorflow，所以压缩时候不考虑Tensorflow）：

可以看到，代码+依赖的大小已经超过上传的50Mzip限制，所以，我们可以在北京区开一个对象存储，然后将我们的代码和训练好的模型上传：

然后我们在北京区，新建一个云函数：

建立之后，我们还要修改内存和超时时间：

然后，我们还需要对index.py进行简单的改造，让其符合云函数的规则：

# -*- coding: utf-8 -*-
from imageai.Prediction import ImagePrediction

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