图像场景识别是DL＋计算机视觉处理的入门程序之一，因此在构建AI展示框架的第一步，则是实现基于flask的图像场景识别。
整个过程非常简单，步骤如下:
（1）设置图像选择和上传的HTML脚本
代码语言：javascript复制<div class="box box-primary">
<form role="form" action="{{ url_for('cv_image_recognize') }}" method="post" enctype="multipart/form-data" >
<div class="box-body">
<div class="form-group">
<label>图像场景识别模型：</label>
<select class="form-control" name='selectmodel'>
{% for modelname in modelnames %}
<option>{{ modelname }}</option>
{% endfor %}
</select>
</div>
<div class="form-group">
<label>返回结果TopK值：</label>
<select class="form-control" name='topk'>
<option>1</option>
<option>3</option>
<option>5</option>
<option selected="selected" >10</option>
</select>
</div>
<div class="form-group">
<label for="exampleInputFile">选择一个图像(.jpg, .png, .bmp等)</label>
<input type="file" name=file id="exampleInputFile" value="选择图像" >
</div>
</div>
<div class="box-footer">
<input type="submit" value="上传并预测" name="predict"/>
</div>
</form>
</div>（2）设置响应的程序
代码语言：javascript复制 @app.route('/cv_image_recognize', methods=['GET', 'POST'])
def cv_image_recognize():
print('upload image and predict...')
#print(request.method)
if request.method == 'POST':
if 'file' not in request.files:
print('No file part')
return redirect(request.url)
file = request.files['file']
arch = request.form['selectmodel']
topk = 5
try:
topk = int(request.form['topk'])
except:
topk = 5
# 上传文件到static文件夹中
uploadfile = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
if file and func.allowed_file(file.filename):
file.save(uploadfile)
#with tempfile.NamedTemporaryFile() as temp:
# form.input_photo.data.save(temp)
# temp.flush()
#image_binary = image.read()
classlist,totaltime = imagenet_logits.RecognizeScene(ai_dir,arch, uploadfile, topk)#detect_objects(temp.name)
photo_form = PhotoForm(request.form)
return render_template('cv/image_recognize.html', header='图片场景识别',description='利用深度学习 imagenet来实现自然图像场景的分类识别,即what is in the picture。',
update_date='2019-04-15',current_user=current_user,
modelnames=model_list,
filename=file.filename, classlist=classlist,totaltime=totaltime)
else:
return render_template('cv/image_recognize.html', header='图片场景识别',description='利用深度学习 imagenet来实现自然图像场景的分类识别,即what is in the picture。',
update_date='2019-04-15',current_user=current_user,
filename='',modelnames=model_list, result={})（3）主要算法处理模块
采用pretrained-models.pytorch来实现，该程序集成了多种模型，是一个非常不错的图像识别框架。
具体实现效果如下所示：
在具体实现过程中，可以动态允许用户选择任意一种模型:
['alexnet', 'bninception', 'cafferesnet101', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'dpn107', 'dpn131', 'dpn68', 'dpn68b', 'dpn92', 'dpn98', 'fbresnet152', 'inceptionresnetv2', 'inceptionv3', 'inceptionv4', 'nasnetalarge', 'nasnetamobile', 'pnasnet5large', 'polynet', 'resnet101', 'resnet152', 'resnet18', 'resnet34', 'resnet50', 'resnext101_32x4d', 'resnext101_64x4d', 'se_resnet101', 'se_resnet152', 'se_resnet50', 'se_resnext101_32x4d', 'se_resnext50_32x4d', 'senet154', 'squeezenet1_0', 'squeezenet1_1', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', 'xception']
并支持用户设置返回的topk的个数。
