Update app.py

app.py

@@ -11,65 +11,68 @@ from tensorflow.keras.utils import to_categorical
 from tensorflow.keras.applications.resnet_v2 import preprocess_input
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 
+# 金門具有代表性的栗喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺五種物種。我們來挑戰五種類別總共用五十張照片, 看能不能打造一個神經網路學會辨識這五種類別。
+# 讀入栗喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺資料圖檔
 image_folders = ['King_Crab', 'Wind_Lion_God', 'pavo_cristatus', 'otter', 'Upupa_epops']
+
+# 為了後面的需要，我們將五種類別照片的答案用 `labels` 呈現
 labels = ["鱟", "金門風獅爺", "金門藍孔雀", "歐亞水獺", "金門戴勝鳥"]
 
-base_dir = './'
+num_classes = len(labels)
 
-thedir = base_dir + image_folders[0]
-os.listdir(thedir)
+base_dir = './classify_image/'
 
-data = []
-target = []
-for i in range(5):
-    thedir = base_dir + image_folders[i]
-    image_fnames = os.listdir(thedir)
-    for theimage in image_fnames:
-        if theimage == ".git" or theimage == ".ipynb_checkpoints":
-          continue
-        img_path = thedir + '/' + theimage
-        img = load_img(img_path , target_size = (256,256))
-        x = img_to_array(img)
-        data.append(x)
-        target.append(i)
+# thedir = base_dir + image_folders[0]
+# os.listdir(thedir)
 
-model = load_model('my_cnn_model.pb') # Loading the Tensorflow Saved Model (PB)
+# data = []
+# target = []
+# for i in range(5):
+#     thedir = base_dir + image_folders[i]
+#     image_fnames = os.listdir(thedir)
+#     for theimage in image_fnames:
+#         if theimage == ".git" or theimage == ".ipynb_checkpoints":
+#           continue
+#         img_path = thedir + '/' + theimage
+#         img = load_img(img_path , target_size = (256,256))
+#         x = img_to_array(img)
+#         data.append(x)
+#         target.append(i)
+
+# 載入並檢視訓練完成的模型。
+
+model = load_model('my_cnn_model.h5') # Loading the Tensorflow Saved Model (PB)
 print(model.summary())
 
+# 注意現在主函數做辨識只有五個種類。而且是使用我們自行訓練的 model!
+
 def classify_image(inp):
     inp = inp.reshape((-1, 256, 256, 3))
     inp = preprocess_input(inp)
     prediction = model.predict(inp).flatten()
     return {labels[i]: float(prediction[i]) for i in range(5)}
 
-image = gr.Image(shape=(256, 256), label="金門藍孔雀、歐亞水獺、戴勝鳥照片")
-label = gr.Label(num_top_classes=5, label="AI辨識結果")
-some_text="我能辨識金門藍孔雀、歐亞水獺、戴勝鳥。找張金門藍孔雀、歐亞水獺、戴勝鳥照片來考我吧!"
+image = gr.Image(shape=(256, 256), label="栗喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺照片")
+label = gr.Label(num_top_classes=num_classes, label="AI ResNet50V2遷移式學習辨識結果")
+some_text="我能辨識金門栗喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺。找張金門栗喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺照片來考我吧!"
+
+# 我們將金門栗喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺數據庫中的圖片拿出來當作範例圖片讓使用者使用
 
 sample_images = []
-for i in range(5):
+for i in range(num_classes):
     thedir = base_dir + image_folders[i]
     for file in os.listdir(thedir):
         if file == ".git" or file == ".ipynb_checkpoints":
           continue
-        sample_images.append(image_folders[i] + '/' + file)
+        sample_images.append(base_dir + image_folders[i] + '/' + file)
 
+# 最後，將所有東西組裝在一起，就大功告成了！
+        
 iface = gr.Interface(fn=classify_image,
              inputs=image,
              outputs=label,
-             title="AI 金門藍孔雀、歐亞水獺、戴勝鳥辨識機",
+             title="AI 喉蜂虎、藍孔雀、戴勝、鱟及歐亞水獺辨識機"",
              description=some_text,
              examples=sample_images, live=True)
 
-# def greet(name):
-
-#     model = load_model('my_cnn_model.h5') # Loading the Tensorflow Saved Model (PB)    
-
-    
-#     return "Hello " + name + "!!" + model.summary()
-
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-
-# .launch(share=True)
 iface.launch()