最近在學習ImageAI對圖片的處理，在此記錄下學習過程。
目標：用自己的圖片庫實現液晶屏異常預測。
1. 將自己的圖片整理成224×224（模型默認）像素的大小，新建一個tf-train目錄（名字隨便起），在該目錄下分爲train和test兩個目錄，目錄下將正常的圖片放在normal文件夾下，將存在異常的圖片放在abnormal文件夾下，文件夾的名稱就是圖片類別的名稱。
——>
——>在train和test目錄下都有
這兩個文件夾，train目錄下共有512張圖片，test有64張。
2.新建一個模型訓練的.py文件
from imageai.Prediction.Custom import ModelTraining
model_trainer = ModelTraining()
model_trainer.setModelTypeAsResNet()
model_trainer.setDataDirectory("tf-train")
model_trainer.trainModel(num_objects=2, num_experiments=100, enhance_data=True, batch_size=4, show_network_summary=True)
由於本項目只有兩類目標，所以num_object = 2, 設置迭代訓練100次後停止，batch_size可以根據GPU內存大小適當設置，我一開始設置32GPU跑步起來，所以改爲4了。
開始等待模型迭代優化，共迭代100次，每次訓練次數：圖片數量/batch=512/4=128
3.訓練完成後，會在tf-train目錄下生產兩個文件夾，joson文件夾下放的是類別標籤，models文件夾下放的是100次的訓練模型。
4.實現預測
from imageai.Prediction.Custom import CustomImagePrediction
import os
import glob
import numpy as np
execution_path = os.getcwd()
# 指定訓練好的模型
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "model_ex-100_acc-1.000000.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=2)
# 三種方式
# 1.預測當前路徑下的指定圖片
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "t4.jpg"), result_count=1)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction, " : ", eachProbability)
# 2.預測當前路徑指定文件夾下所有圖片 predictImage
# for files in glob.glob(os.path.join(execution_path, r"tf-train\test\normal\*.jpg")):
# filepath, filename = os.path.split(files)
# predictions, probabilities = prediction.predictImage(files, result_count=1)
# for eachPrediction, eachProbability in zip(predictions, probabilities):
# print(filename, " predicted is -->", eachPrediction, " : ", eachProbability)
# # 3.預測當前路徑指定文件夾下所有圖片 predictMultipleImages
# imgpathls=[]
# imgnamels=[]
# for img in glob.glob(os.path.join(execution_path, r"tf-train\test\normal\*.jpg")):
# imgpathls.append(img)
# imgnamels.append(os.path.basename(img))
# output = prediction.predictMultipleImages(np.array(imgpathls))
# for k in np.arange(0, len(output)):
# print(imgnamels[k], " predicted is -->", results_array[k]["predictions"][0], " : ", results_array[k]["percentage_probabilities"][0])
結果如下：
還有很好的資料值得學習：
- Somshubra Majumdar, DenseNet Implementation of the paper, Densely Connected Convolutional Networks in Keras
https://github.com/titu1994/DenseNet/ - Broad Institute of MIT and Harvard, Keras package for deep residual networks
https://github.com/broadinstitute/keras-resnet - Fizyr, Keras implementation of RetinaNet object detection
https://github.com/fizyr/keras-retinanet - Francois Chollet, Keras code and weights files for popular deeplearning models
https://github.com/fchollet/deep-learning-models - Forrest N. et al, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://arxiv.org/abs/1602.07360 - Kaiming H. et al, Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385 - Szegedy. et al, Rethinking the Inception Architecture for Computer Vision
https://arxiv.org/abs/1512.00567 - Gao. et al, Densely Connected Convolutional Networks
https://arxiv.org/abs/1608.06993 - Tsung-Yi. et al, Focal Loss for Dense Object Detection
https://arxiv.org/abs/1708.02002 - O Russakovsky et al, ImageNet Large Scale Visual Recognition Challenge
https://arxiv.org/abs/1409.0575 - TY Lin et al, Microsoft COCO: Common Objects in Context
https://arxiv.org/abs/1405.0312 - Moses & John Olafenwa, A collection of images of identifiable professionals.
https://github.com/OlafenwaMoses/IdenProf