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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+classify_image/Image03/Image_03_10.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from tensorflow.keras.applications import ResNet50V2
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+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 = ['Image01', 'Image02', 'Image03']
+
+# 為了後面的需要，我們將五種類別照片的答案用 `labels` 呈現
+labels = ["栗喉蜂虎", "戴勝", "鸕鶿"]
+
+num_classes = len(labels)
+
+base_dir = './classify_image/'
+
+# 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(num_classes)}
+
+image = gr.Image(shape=(256, 256), label="栗喉蜂虎、戴勝及鸕鶿照片")
+label = gr.Label(num_top_classes=num_classes, label="AI ResNet50V2遷移式學習辨識結果")
+some_text="我能辨識栗喉蜂虎、戴勝及鸕鶿。找張栗喉蜂虎、戴勝及鸕鶿照片來考我吧!"
+
+# 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(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(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 栗喉蜂虎、戴勝及鸕鶿辨識機",
+             description=some_text,
+             examples=sample_images, live=True)
+
+iface.launch()

my_cnn_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4c39eec74cac34f5820440f4e6e5abcb9265e0c36064c421f41fb359fbd7e68
+size 94652064