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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