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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
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from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing.image import load_img, img_to_array
1. 讀入資料集¶
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!wget --no-check-certificate \
https://github.com/yenlung/Python-AI-Book/raw/main/dataset/Cooper_img.zip \
-O /content/Cooper.zip
--2022-08-16 04:09:13-- https://github.com/yenlung/Python-AI-Book/raw/main/dataset/Cooper_img.zip Resolving github.com (github.com)... 20.205.243.166 Connecting to github.com (github.com)|20.205.243.166|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/yenlung/Python-AI-Book/main/dataset/Cooper_img.zip [following] --2022-08-16 04:09:13-- https://raw.githubusercontent.com/yenlung/Python-AI-Book/main/dataset/Cooper_img.zip Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 1023448 (999K) [application/zip] Saving to: ‘/content/Cooper.zip’ /content/Cooper.zip 100%[===================>] 999.46K --.-KB/s in 0.008s 2022-08-16 04:09:13 (129 MB/s) - ‘/content/Cooper.zip’ saved [1023448/1023448]
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import os
import zipfile
local_zip = '/content/Cooper.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/content')
zip_ref.close()
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base_dir = '/content/'
cooper_dir = 'Cooper/'
thedir = base_dir + cooper_dir
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cooper = [f"{cooper_dir}/cooper0{i}.jpg" for i in range(1, 7)]
2. 讀入一隻 Cooper 試試¶
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img = load_img(cooper[2], target_size = (224, 224))
plt.imshow(img)
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<matplotlib.image.AxesImage at 0x7f5817bd5e50>
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x = img_to_array(img)
3. 讀入 ResNet50 及 labels¶
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with open('imagenet-classes.txt') as f:
labels = [line.strip() for line in f.readlines()]
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labels[:10]
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['tench 鯉魚類淡水魚', 'goldfish 金魚', 'great white shark 大白鯊', 'tiger shark 虎鯊', 'hammerhead 鎚頭鯊', 'electric ray 電魟', 'stingray 魟魚', 'cock 公雞', 'hen 母雞', 'ostrich 鴕鳥']
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model = ResNet50()
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x = img_to_array(img).reshape(1, 224, 224, 3)
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inp = preprocess_input(x)
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outp = model.predict([inp])
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y_pred = np.argmax(model.predict(inp), axis=-1)[0]
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print(f"ResNet 覺得是 {labels[y_pred]}")
ResNet 覺得是 Maltese dog 馬爾濟斯犬
4. 做成 Web App!¶
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!pip install gradio
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import gradio as gr
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def classify_image(inp):
inp = inp.reshape((-1, 224, 224, 3))
inp = preprocess_input(inp)
prediction = resnet.predict(inp).flatten()
return {labels[i]: float(prediction[i]) for i in range(1000)}
image = gr.inputs.Image(shape=(224, 224), label="請上傳一張圖片")
label = gr.outputs.Label(num_top_classes=3, label="模型猜測的答案是：")
sample_images = [[f'cooper0{i}.jpg'] for i in range(1, 7)]
gr.Interface(fn=classify_image,
inputs=image,
outputs=label,
examples=sample_images).launch(share=True)
Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()` Running on public URL: https://14329.gradio.app This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)
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(<fastapi.applications.FastAPI at 0x7f662599b6d0>, 'http://127.0.0.1:7860/', 'https://14329.gradio.app')
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