[Fix] Update README in #41
geoyee
3 years ago
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# [PaddleRS：无人机汽车识别](https://aistudio.baidu.com/aistudio/projectdetail/3713122) |
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基于0.5m的高分辨率无人机影像，我们希望能够使用目标检测的方法找到影像中的汽车。项目将基于PaddleRS完成该任务。 |
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## 1 数据准备 |
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数据来自于[DFC2018 Houston](https://hyperspectral.ee.uh.edu/?page_id=1075)，裁剪为1400张596x601大小的图块，由手工标注而成并按照9:1划分训练集和数据集。 |
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```python |
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# 解压数据集 |
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! mkdir -p dataset |
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! unzip -oq data/data56250/carDetection_RGB.zip -d dataset |
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``` |
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```python |
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# 划分数据集 |
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import os |
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import os.path as osp |
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import random |
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def get_data_list(data_dir): |
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random.seed(666) |
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mode = ["train_list", "val_list"] |
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dir_path = osp.join(data_dir, "JPEGImages") |
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files = [f.split(".")[0] for f in os.listdir(dir_path)] |
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random.shuffle(files) # 打乱顺序 |
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with open(osp.join(data_dir, f"{mode[0]}.txt"), "w") as f_tr: |
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with open(osp.join(data_dir, f"{mode[1]}.txt"), "w") as f_va: |
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for i, name in enumerate(files): |
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if (i % 10) == 0: # 训练集与测试集为9:1 |
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f_va.write(f"JPEGImages/{name}.jpg Annotations/{name}.xml\n") |
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else: |
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f_tr.write(f"JPEGImages/{name}.jpg Annotations/{name}.xml\n") |
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labels = ["car"] |
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txt_str = "\n".join(labels) |
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with open((data_dir + "/" + f"label_list.txt"), "w") as f: |
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f.write(txt_str) |
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print("Finished!") |
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get_data_list("dataset") |
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``` |
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## 2 PaddleRS准备 |
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PaddleRS是基于飞桨开发的遥感处理平台，支持遥感图像分类，目标检测，图像分割，以及变化检测等常用遥感任务，帮助开发者更便捷地完成从训练到部署全流程遥感深度学习应用。 |
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github：[https://github.com/PaddleCV-SIG/PaddleRS](https://github.com/PaddleCV-SIG/PaddleRS) |
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```python |
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! git clone https://github.com/PaddleCV-SIG/PaddleRS.git |
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! pip install -q -r PaddleRS/requirements.txt |
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import sys |
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sys.path.append("PaddleRS") |
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``` |
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## 3 模型训练 |
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PaddleRS借鉴PaddleSeg的API设计模式并进行了较高程度的封装，可以方便的完成数据、模型等的定义，快速开始模型的训练迭代。 |
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### 3.1 数据定义 |
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主要通过`datasets`和`transforms`两个组件完成任务，`datasets`中有包含分割检测分类等多任务的数据加载API，而`transforms`集成了大部分通用或单独的数据增强API，目前可以通过源码查看。 |
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```python |
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import os |
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import os.path as osp |
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from paddlers.datasets import VOCDetection |
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from paddlers import transforms as T |
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# 定义数据增强 |
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train_transforms = T.Compose([ |
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T.RandomDistort(), |
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T.RandomCrop(), |
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T.RandomHorizontalFlip(), |
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T.BatchRandomResize( |
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target_sizes=[512, 544, 576, 608, 640, 672, 704], |
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interp='RANDOM'), |
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T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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]) |
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eval_transforms = T.Compose([ |
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T.Resize(target_size=608, interp='CUBIC'), |
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T.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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]) |
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# 定义数据集 |
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data_dir = "dataset" |
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train_file_list = osp.join(data_dir, 'train_list.txt') |
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val_file_list = osp.join(data_dir, 'val_list.txt') |
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label_file_list = osp.join(data_dir, 'label_list.txt') |
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train_dataset = VOCDetection( |
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data_dir=data_dir, |
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file_list=train_file_list, |
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label_list=label_file_list, |
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transforms=train_transforms, |
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shuffle=True) |
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eval_dataset = VOCDetection( |
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data_dir=data_dir, |
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file_list=train_file_list, |
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label_list=label_file_list, |
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transforms=eval_transforms, |
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shuffle=False) |
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``` |
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### 3.2 模型准备 |
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PaddleRS将模型分别放置于`models`和`custom_models`中，分别包含了Paddle四大套件的模型结构以及与遥感、变化检测等相关的模型结构。通过`tasks`进行了模型的封装，集成了Loss、Opt、Metrics等，可根据需要进行修改。这里以默认的PPYOLOv2为例。 |
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```python |
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from paddlers.tasks.object_detector import PPYOLOv2 |
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num_classes = len(train_dataset.labels) |
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model = PPYOLOv2(num_classes=num_classes) |
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``` |
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```python |
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model.train( |
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num_epochs=30, |
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train_dataset=train_dataset, |
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train_batch_size=16, |
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eval_dataset=eval_dataset, |
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pretrain_weights="COCO", |
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learning_rate=3e-5, |
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warmup_steps=10, |
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warmup_start_lr=0.0, |
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save_interval_epochs=5, |
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lr_decay_epochs=[10, 20], |
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save_dir="output", |
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use_vdl=True) |
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``` |
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## 4 模型评估 |
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只需要调用evaluate即可完成预测。 |
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```python |
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model.evaluate(eval_dataset) |
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``` |
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返回如下输出。 |
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``` |
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2022-03-30 19:59:13 [INFO] Start to evaluate(total_samples=944, total_steps=944)... |
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2022-03-30 20:00:05 [INFO] Accumulating evaluatation results... |
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OrderedDict([('bbox_map', 90.33284968764544)]) |
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``` |
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## 5 模型预测 |
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PaddleRS的目标检测task可以方便的给出坐标、类别和分数，可供自行进行一些后处理。也可以直接使用visualize_detection进行可视化。下面对一张测试图像进行预测并可视化。 |
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```python |
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from paddlers.tasks.utils.visualize import visualize_detection |
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import matplotlib.pyplot as plt |
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%matplotlib inline |
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img_path = "dataset/JPEGImages/UH_NAD83_272056_3289689_58.jpg" |
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pred = model.predict(img_path, eval_transforms) |
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vis_img = visualize_detection(img_path, pred, save_dir=None) |
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plt.figure(figsize=(10, 10)) |
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plt.imshow(vis_img) |
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plt.show() |
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``` |
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## 总结 |
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- 这里PPYOLOv2的效果很不错，后续在目标检测方面，将会为PaddleRS增加滑框预测以及GeoJSON等数据格式的导出。 |
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