今天小編就爲大家分享一篇與Django結合利用模型對上傳圖片預測詳解,具有很好的參考價值,希望對大家有所幫助。一起跟隨小編過來看看吧
1 預處理
(1)對上傳的圖片進行預處理成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)將圖片轉化成數組
def read_image2(filename): img = Image.open('./media/pic/'+filename).convert('RGB') return np.array(img)
2 利用模型進行預測
def testcat(picname): # 預處理圖片 變成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反覆識別注意 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 = ['布偶貓', '孟買貓', '暹羅貓', '英國短毛貓'] # print(target[classes]) return classes
3 與Django結合
在views中調用模型進行圖片分類
def catinfo(request): if request.method == "POST": f1 = request.FILES['pic1'] # 用於識別 fname = '%s/pic/%s' % (settings.MEDIA_ROOT, f1.name) with open(fname, 'wb') as pic: for c in f1.chunks(): pic.write(c) # 用於顯示 fname1 = './static/img/%s' % f1.name with open(fname1, 'wb') as pic: for c in f1.chunks(): pic.write(c) num = testcat(f1.name) # 有的數據庫id從1開始這樣就會報錯 # 因此原本數據庫中的id=0被系統改爲id=4 # 遇到這樣的問題就加上 # if(num == 0): # num = 4 # 通過id獲取貓的信息 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("上傳失敗!")
以上這篇與Django結合利用模型對上傳圖片預測的實例詳解就是小編分享給大家的全部內容了,希望能給大家一個參考,也希望大家多多支持神馬文庫。