与Django结合利用模型对上传图片预测的实例详解
1 预处理
（1）对上传的图片进行预处理成100*100大小
def prepicture(picname):
img = Image.open('./media/pic/' + picname)
new_img = img.resize((100, 100), Image.BILINEAR)
new_img.save(os.path.join('./media/pic/', os.path.basename(picname)))
（2）将图片转化成数组
def read_image2(filename):
img = Image.open('./media/pic/'+filename).convert('RGB')
return np.array(img)
2 利用模型进行预测
def testcat(picname):
# 预处理图片 变成100 x 100
prepicture(picname)
x_test = []
x_test.append(read_image2(picname))
x_test = np.array(x_test)
x_test = x_test.astype('float32')
x_test /= 255
keras.backend.clear_session() #清理session反复识别注意
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.load_weights('./cat/cat_weights.h5')
classes = model.predict_classes(x_test)[0]
# target = ['布偶猫', '孟买猫', '暹罗猫', '英国短毛猫']
# print(target[classes])
return classes
3 与Django结合
在views中调用模型进行图片分类
def catinfo(request):
if request.method == "POST":
f1 = request.FILES['pic1']
# 用于识别
fname = '%s/pic/%s' % (settings.MEDIA_ROOT, f1.name)
with open(fname, 'wb') as pic:
for c in f1.chunks():
pic.write(c)
# 用于显示
fname1 = './static/img/%s' % f1.name
with open(fname1, 'wb') as pic:
for c in f1.chunks():
pic.write(c)
num = testcat(f1.name)
# 有的数据库id从1开始这样就会报错
# 因此原本数据库中的id=0被系统改为id=4
# 遇到这样的问题就加上
# if(num == 0):
# num = 4
# 通过id获取猫的信息
name = models.Catinfo.objects.get(id = num)
return render(request, 'info.html', {'nameinfo': name.nameinfo, 'feature': name.feature, 'livemethod': name.livemethod, 'feednn': name.feednn, 'feedmethod': name.feedmethod, 'picname': f1.name})
else:
return HttpResponse("上传失败！")
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