[Enhance] Enhance vis-pipeline tool. (#604)
* enhance vis-pipeline add intermediate imgs * enhance vis-pipeline add intermediate imgs * improve code of vi-pipeline * modify docs for vis-pipeline * Use `mmcv.utils.digit_version` instead of `distutils` * add size info in the bottom * preform adaptive-resize in before concat * add warning info * fix docs * fix lint * fix comment * fix docs Co-authored-by: mzr1996 <mzr1996@163.com>pull/722/head
Ezra-Yu
GitHub
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779a06257c
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# MISCELLANEOUS
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# Miscellaneous
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<!-- TOC -->
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@ -2,7 +2,6 @@
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<!-- TOC -->
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- [Visualization](#visualization)
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- [Pipeline Visualization](#pipeline-visualization)
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- [Learning Rate Schedule Visualization](#learning-rate-schedule-visualization)
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- [Class Activation Map Visualization](#class-activation-map-visualization)
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@ -14,17 +13,18 @@
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```bash
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python tools/visualizations/vis_pipeline.py \
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${CONFIG_FILE} \
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--output-dir ${OUTPUT_DIR} \
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--phase ${DATASET_PHASE} \
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--number ${BUNBER_IMAGES_DISPLAY} \
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--skip-type ${SKIP_TRANSFORM_TYPE}
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--mode ${DISPLAY_MODE} \
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--show \
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--adaptive \
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--min-edge-length ${MIN_EDGE_LENGTH} \
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--max-edge-length ${MAX_EDGE_LENGTH} \
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--bgr2rgb \
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--window-size ${WINDOW_SIZE}
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[--output-dir ${OUTPUT_DIR}] \
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[--phase ${DATASET_PHASE}] \
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[--number ${BUNBER_IMAGES_DISPLAY}] \
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[--skip-type ${SKIP_TRANSFORM_TYPE}] \
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[--mode ${DISPLAY_MODE}] \
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[--show] \
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[--adaptive] \
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[--min-edge-length ${MIN_EDGE_LENGTH}] \
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[--max-edge-length ${MAX_EDGE_LENGTH}] \
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[--bgr2rgb] \
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[--window-size ${WINDOW_SIZE}] \
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[--cfg-options ${CFG_OPTIONS}]
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```
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**Description of all arguments**：
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@ -32,48 +32,57 @@ python tools/visualizations/vis_pipeline.py \
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- `config` : The path of a model config file.
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- `--output-dir`: The output path for visualized images. If not specified, it will be set to `''`, which means not to save.
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- `--phase`: Phase of visualizing dataset，must be one of `[train, val, test]`. If not specified, it will be set to `train`.
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- `--number`: The number of samples to visualize. If not specified, display all images in the dataset.
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- `--number`: The number of samples to visualized. If not specified, display all images in the dataset.
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- `--skip-type`: The pipelines to be skipped. If not specified, it will be set to `['ToTensor', 'Normalize', 'ImageToTensor', 'Collect']`.
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- `--mode`: The display mode, can be one of `[original, pipeline, concat]`. If not specified, it will be set to `concat`.
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- `--show`: If set, display pictures in pop-up windows.
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- `--adaptive`: If set, automatically adjust the size of the visualization images.
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- `--adaptive`: If set, adaptively resize images for better visualization.
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- `--min-edge-length`: The minimum edge length, used when `--adaptive` is set. When any side of the picture is smaller than `${MIN_EDGE_LENGTH}`, the picture will be enlarged while keeping the aspect ratio unchanged, and the short side will be aligned to `${MIN_EDGE_LENGTH}`. If not specified, it will be set to 200.
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- `--max-edge-length`: The maximum edge length, used when `--adaptive` is set. When any side of the picture is larger than `${MAX_EDGE_LENGTH}`, the picture will be reduced while keeping the aspect ratio unchanged, and the long side will be aligned to `${MAX_EDGE_LENGTH}`. If not specified, it will be set to 1000.
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- `--bgr2rgb`: If set, flip the color channel order of images.
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- `--window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`.
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- `--cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html).
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```{note}
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1. If the `--mode` is not specified, it will be set to `concat` as default, get the pictures stitched together by original pictures and transformed pictures; if the `--mode` is set to `original`, get the original pictures; if the `--mode` is set to `pipeline`, get the transformed pictures.
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1. If the `--mode` is not specified, it will be set to `concat` as default, get the pictures stitched together by original pictures and transformed pictures; if the `--mode` is set to `original`, get the original pictures; if the `--mode` is set to `transformed`, get the transformed pictures; if the `--mode` is set to `pipeline`, get all the intermediate images through the pipeline.
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2. When `--adaptive` option is set, images that are too large or too small will be automatically adjusted, you can use `--min-edge-length` and `--max-edge-length` to set the adjust size.
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```
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**Examples**：
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1. Visualize all the transformed pictures of the `ImageNet` training set and display them in pop-up windows：
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```shell
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python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode pipeline
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```
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<div align=center><img src="../_static/image/tools/visualization/pipeline-pipeline.jpg" style=" width: auto; height: 40%; "></div>
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2. Visualize 10 comparison pictures in the `ImageNet` train set and save them in the `./tmp` folder：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive
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```
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<div align=center><img src="../_static/image/tools/visualization/pipeline-concat.jpg" style=" width: auto; height: 40%; "></div>
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3. Visualize 100 original pictures in the `CIFAR100` validation set, then display and save them in the `./tmp` folder：
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1. In **'original'** mode, visualize 100 original pictures in the `CIFAR100` validation set, then display and save them in the `./tmp` folder：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb
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```
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<div align=center><img src="../_static/image/tools/visualization/pipeline-original.jpg" style=" width: auto; height: 40%; "></div>
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146117528-1ec2d918-57f8-4ae4-8ca3-a8d31b602f64.jpg" style=" width: auto; height: 40%; "></div>
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2. In **'transformed'** mode, visualize all the transformed pictures of the `ImageNet` training set and display them in pop-up windows：
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```shell
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python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed
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```
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146117553-8006a4ba-e2fa-4f53-99bc-42a4b06e413f.jpg" style=" width: auto; height: 40%; "></div>
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3. In **'concat'** mode, visualize 10 pairs of origin and transformed images for comparison in the `ImageNet` train set and save them in the `./tmp` folder：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive
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```
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146128259-0a369991-7716-411d-8c27-c6863e6d76ea.JPEG" style=" width: auto; height: 40%; "></div>
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4. In **'pipeline'** mode, visualize all the intermediate pictures in the `ImageNet` train set through the pipeline：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show
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```
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146128201-eb97c2aa-a615-4a81-a649-38db1c315d0e.JPEG" style=" width: auto; height: 40%; "></div>
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## Learning Rate Schedule Visualization
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<!-- TOC -->
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- [可视化](#可视化)
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- [数据流水线可视化](#数据流水线可视化)
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- [学习率策略可视化](#学习率策略可视化)
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- [类别激活图可视化](#类别激活图可视化)
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@ -15,17 +14,18 @@
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```bash
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python tools/visualizations/vis_pipeline.py \
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${CONFIG_FILE} \
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--output-dir ${OUTPUT_DIR} \
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--phase ${DATASET_PHASE} \
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--number ${BUNBER_IMAGES_DISPLAY} \
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--skip-type ${SKIP_TRANSFORM_TYPE} \
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--mode ${DISPLAY_MODE} \
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--show \
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--adaptive \
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--min-edge-length ${MIN_EDGE_LENGTH} \
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--max-edge-length ${MAX_EDGE_LENGTH} \
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--bgr2rgb \
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--window-size ${WINDOW_SIZE}
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[--output-dir ${OUTPUT_DIR}] \
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[--phase ${DATASET_PHASE}] \
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[--number ${BUNBER_IMAGES_DISPLAY}] \
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[--skip-type ${SKIP_TRANSFORM_TYPE}] \
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[--mode ${DISPLAY_MODE}] \
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[--show] \
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[--adaptive] \
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[--min-edge-length ${MIN_EDGE_LENGTH}] \
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[--max-edge-length ${MAX_EDGE_LENGTH}] \
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[--bgr2rgb] \
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[--window-size ${WINDOW_SIZE}] \
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[--cfg-options ${CFG_OPTIONS}]
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```
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**所有参数的说明**：
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@ -35,71 +35,80 @@ python tools/visualizations/vis_pipeline.py \
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- `--phase`: 可视化数据集的阶段，只能为 `[train, val, test]` 之一，默认为 `train`。
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- `--number`: 可视化样本数量。如果没有指定，默认展示数据集的所有图片。
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- `--skip-type`: 预设跳过的数据流水线过程。如果没有指定，默认为 `['ToTensor', 'Normalize', 'ImageToTensor', 'Collect']`。
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- `--mode`: 可视化的模式，只能为 `[original, pipeline, concat]` 之一，如果没有指定，默认为 `concat`。
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- `--mode`: 可视化的模式，只能为 `[original, transformed, concat, pipeline]` 之一，如果没有指定，默认为 `concat`。
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- `--show`: 将可视化图片以弹窗形式展示。
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- `--adaptive`: 自动调节可视化图片的大小。
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- `--min-edge-length`: 最短边长度，当使用了 `--adaptive` 时有效。 当图片任意边小于 `${MIN_EDGE_LENGTH}` 时，会保持长宽比不变放大图片，短边对齐至 `${MIN_EDGE_LENGTH}`，默认为200。
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- `--max-edge-length`: 最长边长度，当使用了 `--adaptive` 时有效。 当图片任意边大于 `${MAX_EDGE_LENGTH}` 时，会保持长宽比不变缩小图片，短边对齐至 `${MAX_EDGE_LENGTH}`，默认为1000。
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- `--bgr2rgb`: 将图片的颜色通道翻转。
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- `--window-size`: 可视化窗口大小，如果没有指定，默认为 `12*7`。如果需要指定，按照格式 `'W*H'`。
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- `--cfg-options` : 对配置文件的修改，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。
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```{note}
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1. 如果不指定 `--mode`，默认设置为 `concat`，获取原始图片和预处理后图片拼接的图片；如果 `--mode` 设置为 `original`，则获取原始图片； 如果 `--mode` 设置为 `pipeline`，则获取预处理后的图片。
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1. 如果不指定 `--mode`，默认设置为 `concat`，获取原始图片和预处理后图片拼接的图片；如果 `--mode` 设置为 `original`，则获取原始图片；如果 `--mode` 设置为 `transformed`，则获取预处理后的图片；如果 `--mode` 设置为 `pipeline`，则获得数据流水线所有中间过程图片。
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2. 当指定了 `--adaptive` 选项时，会自动的调整尺寸过大和过小的图片，你可以通过设定 `--min-edge-length` 与 `--max-edge-length` 来指定自动调整的图片尺寸。
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```
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**示例**：
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1. 可视化 `ImageNet` 训练集的所有经过预处理的图片，并以弹窗形式显示：
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```shell
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python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode pipeline
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```
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<div align=center><img src="../_static/image/tools/visualization/pipeline-pipeline.jpg" style=" width: auto; height: 40%; "></div>
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2. 可视化 `ImageNet` 训练集的10张原始图片与预处理后图片对比图，保存在 `./tmp` 文件夹下：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive
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```
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<div align=center><img src="../_static/image/tools/visualization/pipeline-concat.jpg" style=" width: auto; height: 40%; "></div>
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3. 可视化 `CIFAR100` 验证集中的100张原始图片，显示并保存在 `./tmp` 文件夹下：
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1. **'original'** 模式，可视化 `CIFAR100` 验证集中的100张原始图片，显示并保存在 `./tmp` 文件夹下：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb
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```
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<div align=center><img src="../_static/image/tools/visualization/pipeline-original.jpg" style=" width: auto; height: 40%; "></div>
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146117528-1ec2d918-57f8-4ae4-8ca3-a8d31b602f64.jpg" style=" width: auto; height: 40%; "></div>
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2. **'transformed'** 模式，可视化 `ImageNet` 训练集的所有经过预处理的图片，并以弹窗形式显示：
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```shell
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python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed
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```
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146117553-8006a4ba-e2fa-4f53-99bc-42a4b06e413f.jpg" style=" width: auto; height: 40%; "></div>
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3. **'concat'** 模式，可视化 `ImageNet` 训练集的10张原始图片与预处理后图片对比图，保存在 `./tmp` 文件夹下：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive
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```
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146128259-0a369991-7716-411d-8c27-c6863e6d76ea.JPEG" style=" width: auto; height: 40%; "></div>
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4. **'pipeline'** 模式，可视化 `ImageNet` 训练集经过数据流水线的过程图像：
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```shell
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python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show
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```
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<div align=center><img src="https://user-images.githubusercontent.com/18586273/146128201-eb97c2aa-a615-4a81-a649-38db1c315d0e.JPEG" style=" width: auto; height: 40%; "></div>
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## 学习率策略可视化
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```bash
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python tools/visualizations/vis_lr.py \
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${CONFIG_FILE} \
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--dataset-size ${Dataset_Size} \
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--ngpus ${NUM_GPUs}
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--save-path ${SAVE_PATH} \
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--title ${TITLE} \
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--style ${STYLE} \
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--window-size ${WINDOW_SIZE}
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--cfg-options
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[--dataset-size ${Dataset_Size}] \
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[--ngpus ${NUM_GPUs}] \
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[--save-path ${SAVE_PATH}] \
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[--title ${TITLE}] \
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[--style ${STYLE}] \
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[--window-size ${WINDOW_SIZE}] \
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[--cfg-options ${CFG_OPTIONS}] \
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```
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**所有参数的说明**：
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- `config` : 模型配置文件的路径。
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- `dataset-size` : 数据集的大小。如果指定，`build_dataset` 将被跳过并使用这个大小作为数据集大小，默认使用 `build_dataset` 所得数据集的大小。
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- `ngpus` : 使用 GPU 的数量。
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- `save-path` : 保存的可视化图片的路径，默认不保存。
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- `title` : 可视化图片的标题，默认为配置文件名。
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- `style` : 可视化图片的风格，默认为 `whitegrid`。
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- `window-size`: 可视化窗口大小，如果没有指定，默认为 `12*7`。如果需要指定，按照格式 `'W*H'`。
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- `cfg-options` : 对配置文件的修改，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。
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- `--dataset-size` : 数据集的大小。如果指定，`build_dataset` 将被跳过并使用这个大小作为数据集大小，默认使用 `build_dataset` 所得数据集的大小。
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- `--ngpus` : 使用 GPU 的数量。
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- `--save-path` : 保存的可视化图片的路径，默认不保存。
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- `--title` : 可视化图片的标题，默认为配置文件名。
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- `--style` : 可视化图片的风格，默认为 `whitegrid`。
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- `--window-size`: 可视化窗口大小，如果没有指定，默认为 `12*7`。如果需要指定，按照格式 `'W*H'`。
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- `--cfg-options` : 对配置文件的修改，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。
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```{note}
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@ -1,18 +1,27 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import copy
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import itertools
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import os
|
||||
import re
|
||||
import sys
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
|
||||
import cv2
|
||||
import mmcv
|
||||
import numpy as np
|
||||
from mmcv import Config, DictAction, ProgressBar
|
||||
|
||||
from mmcls.core import visualization as vis
|
||||
from mmcls.datasets.builder import build_dataset
|
||||
from mmcls.datasets.pipelines import Compose
|
||||
from mmcls.datasets.builder import PIPELINES, build_dataset, build_from_cfg
|
||||
from mmcls.models.utils import to_2tuple
|
||||
|
||||
# text style
|
||||
bright_style, reset_style = '\x1b[1m', '\x1b[0m'
|
||||
red_text, blue_text = '\x1b[31m', '\x1b[34m'
|
||||
white_background = '\x1b[107m'
|
||||
|
||||
|
||||
def parse_args():
|
||||
|
|
@ -48,11 +57,12 @@ def parse_args():
|
|||
'--mode',
|
||||
default='concat',
|
||||
type=str,
|
||||
choices=['original', 'pipeline', 'concat'],
|
||||
choices=['original', 'transformed', 'concat', 'pipeline'],
|
||||
help='display mode; display original pictures or transformed pictures'
|
||||
' or comparison pictures. "original" means show images load from disk;'
|
||||
' "pipeline" means to show images after pipeline; "concat" means show '
|
||||
'images stitched by "original" and "pipeline" images. Default concat.')
|
||||
' or comparison pictures. "original" means show images load from disk'
|
||||
'; "transformed" means to show images after transformed; "concat" '
|
||||
'means show images stitched by "original" and "output" images. '
|
||||
'"pipeline" means show all the intermediate images. Default concat.')
|
||||
parser.add_argument(
|
||||
'--show',
|
||||
default=False,
|
||||
|
|
@ -71,7 +81,7 @@ def parse_args():
|
|||
'"--adaptive" is true. Default 200.')
|
||||
parser.add_argument(
|
||||
'--max-edge-length',
|
||||
default=1000,
|
||||
default=800,
|
||||
type=int,
|
||||
help='the max edge length when visualizing images, used when '
|
||||
'"--adaptive" is true. Default 1000.')
|
||||
|
|
@ -130,7 +140,7 @@ def retrieve_data_cfg(config_path, skip_type, cfg_options, phase):
|
|||
return cfg
|
||||
|
||||
|
||||
def build_dataset_pipeline(cfg, phase):
|
||||
def build_dataset_pipelines(cfg, phase):
|
||||
"""build dataset and pipeline from config.
|
||||
|
||||
Separate the pipeline except 'LoadImageFromFile' step if
|
||||
|
|
@ -144,43 +154,103 @@ def build_dataset_pipeline(cfg, phase):
|
|||
origin_pipeline = data_cfg.pipeline
|
||||
data_cfg.pipeline = loadimage_pipeline
|
||||
dataset = build_dataset(data_cfg)
|
||||
pipeline = Compose(origin_pipeline)
|
||||
pipelines = {
|
||||
pipeline_cfg['type']: build_from_cfg(pipeline_cfg, PIPELINES)
|
||||
for pipeline_cfg in origin_pipeline
|
||||
}
|
||||
|
||||
return dataset, pipeline
|
||||
return dataset, pipelines
|
||||
|
||||
|
||||
def put_img(board, img, center):
|
||||
"""put a image into a big board image with the anchor center."""
|
||||
center_x, center_y = center
|
||||
img_h, img_w, _ = img.shape
|
||||
xmin, ymin = int(center_x - img_w // 2), int(center_y - img_h // 2)
|
||||
board[ymin:ymin + img_h, xmin:xmin + img_w, :] = img
|
||||
def prepare_imgs(args, imgs: List[np.ndarray], steps=None):
|
||||
"""prepare the showing picture."""
|
||||
ori_shapes = [img.shape for img in imgs]
|
||||
# adaptive adjustment to rescale pictures
|
||||
if args.adaptive:
|
||||
for i, img in enumerate(imgs):
|
||||
imgs[i] = adaptive_size(img, args.min_edge_length,
|
||||
args.max_edge_length)
|
||||
else:
|
||||
# if src image is too large or too small,
|
||||
# warning a "--adaptive" message.
|
||||
for ori_h, ori_w, _ in ori_shapes:
|
||||
if (args.min_edge_length > ori_h or args.min_edge_length > ori_w
|
||||
or args.max_edge_length < ori_h
|
||||
or args.max_edge_length < ori_w):
|
||||
msg = red_text
|
||||
msg += 'The visualization picture is too small or too large to'
|
||||
msg += ' put text information on it, please add '
|
||||
msg += bright_style + red_text + white_background
|
||||
msg += '"--adaptive"'
|
||||
msg += reset_style + red_text
|
||||
msg += ' to adaptively rescale the showing pictures'
|
||||
msg += reset_style
|
||||
warnings.warn(msg)
|
||||
|
||||
if len(imgs) == 1:
|
||||
return imgs[0]
|
||||
else:
|
||||
return concat_imgs(imgs, steps, ori_shapes)
|
||||
|
||||
|
||||
def concat_imgs(imgs, steps, ori_shapes):
|
||||
"""Concat list of pictures into a single big picture, align height here."""
|
||||
show_shapes = [img.shape for img in imgs]
|
||||
show_heights = [shape[0] for shape in show_shapes]
|
||||
show_widths = [shape[1] for shape in show_shapes]
|
||||
|
||||
max_height = max(show_heights)
|
||||
text_height = 20
|
||||
font_size = 0.5
|
||||
pic_horizontal_gap = min(show_widths) // 10
|
||||
for i, img in enumerate(imgs):
|
||||
cur_height = show_heights[i]
|
||||
pad_height = max_height - cur_height
|
||||
pad_top, pad_bottom = to_2tuple(pad_height // 2)
|
||||
# handle instance that the pad_height is an odd number
|
||||
if pad_height % 2 == 1:
|
||||
pad_top = pad_top + 1
|
||||
pad_bottom += text_height * 3 # keep pxs to put step information text
|
||||
pad_left, pad_right = to_2tuple(pic_horizontal_gap)
|
||||
# make border
|
||||
img = cv2.copyMakeBorder(
|
||||
img,
|
||||
pad_top,
|
||||
pad_bottom,
|
||||
pad_left,
|
||||
pad_right,
|
||||
cv2.BORDER_CONSTANT,
|
||||
value=(255, 255, 255))
|
||||
# put transform phase information in the bottom
|
||||
imgs[i] = cv2.putText(
|
||||
img=img,
|
||||
text=steps[i],
|
||||
org=(pic_horizontal_gap, max_height + text_height // 2),
|
||||
fontFace=cv2.FONT_HERSHEY_TRIPLEX,
|
||||
fontScale=font_size,
|
||||
color=(255, 0, 0),
|
||||
lineType=1)
|
||||
# put image size information in the bottom
|
||||
imgs[i] = cv2.putText(
|
||||
img=img,
|
||||
text=str(ori_shapes[i]),
|
||||
org=(pic_horizontal_gap, max_height + int(text_height * 1.5)),
|
||||
fontFace=cv2.FONT_HERSHEY_TRIPLEX,
|
||||
fontScale=font_size,
|
||||
color=(255, 0, 0),
|
||||
lineType=1)
|
||||
|
||||
# Height alignment for concatenating
|
||||
board = np.concatenate(imgs, axis=1)
|
||||
return board
|
||||
|
||||
|
||||
def concat(left_img, right_img):
|
||||
"""Concat two pictures into a single big picture, accepts two images with
|
||||
diffenert shapes."""
|
||||
GAP = 10
|
||||
left_h, left_w, _ = left_img.shape
|
||||
right_h, right_w, _ = right_img.shape
|
||||
# create a big board to contain images with shape (board_h, board_w*2+10)
|
||||
board_h, board_w = max(left_h, right_h), max(left_w, right_w)
|
||||
board = np.ones([board_h, 2 * board_w + GAP, 3], np.uint8) * 255
|
||||
|
||||
put_img(board, left_img, (int(board_w // 2), int(board_h // 2)))
|
||||
put_img(board, right_img,
|
||||
(int(board_w // 2) + board_w + GAP // 2, int(board_h // 2)))
|
||||
return board
|
||||
|
||||
|
||||
def adaptive_size(mode, image, min_edge_length, max_edge_length):
|
||||
"""rescale image if image is too small to put text like cifra."""
|
||||
def adaptive_size(image, min_edge_length, max_edge_length, src_shape=None):
|
||||
"""rescale image if image is too small to put text like cifar."""
|
||||
assert min_edge_length >= 0 and max_edge_length >= 0
|
||||
assert max_edge_length >= min_edge_length
|
||||
|
||||
image_h, image_w, *_ = image.shape
|
||||
image_w = image_w // 2 if mode == 'concat' else image_w
|
||||
src_shape = image.shape if src_shape is None else src_shape
|
||||
image_h, image_w, _ = src_shape
|
||||
|
||||
if image_h < min_edge_length or image_w < min_edge_length:
|
||||
image = mmcv.imrescale(
|
||||
|
|
@ -191,49 +261,58 @@ def adaptive_size(mode, image, min_edge_length, max_edge_length):
|
|||
return image
|
||||
|
||||
|
||||
def get_display_img(item, pipeline, mode, bgr2rgb):
|
||||
def get_display_img(args, item, pipelines):
|
||||
"""get image to display."""
|
||||
if bgr2rgb:
|
||||
# srcs picture could be in RGB or BGR order due to different backends.
|
||||
if args.bgr2rgb:
|
||||
item['img'] = mmcv.bgr2rgb(item['img'])
|
||||
src_image = item['img'].copy()
|
||||
# get transformed picture
|
||||
if mode in ['pipeline', 'concat']:
|
||||
item = pipeline(item)
|
||||
trans_image = item['img']
|
||||
trans_image = np.ascontiguousarray(trans_image, dtype=np.uint8)
|
||||
pipeline_images = [src_image]
|
||||
|
||||
# get intermediate images through pipelines
|
||||
if args.mode in ['transformed', 'concat', 'pipeline']:
|
||||
for pipeline in pipelines.values():
|
||||
item = pipeline(item)
|
||||
trans_image = copy.deepcopy(item['img'])
|
||||
trans_image = np.ascontiguousarray(trans_image, dtype=np.uint8)
|
||||
pipeline_images.append(trans_image)
|
||||
|
||||
# concatenate images to be showed according to mode
|
||||
if args.mode == 'original':
|
||||
image = prepare_imgs(args, [src_image], ['src'])
|
||||
elif args.mode == 'transformed':
|
||||
image = prepare_imgs(args, [pipeline_images[-1]], ['transformed'])
|
||||
elif args.mode == 'concat':
|
||||
steps = ['src', 'transformed']
|
||||
image = prepare_imgs(args, [pipeline_images[0], pipeline_images[-1]],
|
||||
steps)
|
||||
elif args.mode == 'pipeline':
|
||||
steps = ['src'] + list(pipelines.keys())
|
||||
image = prepare_imgs(args, pipeline_images, steps)
|
||||
|
||||
if mode == 'concat':
|
||||
image = concat(src_image, trans_image)
|
||||
elif mode == 'original':
|
||||
image = src_image
|
||||
elif mode == 'pipeline':
|
||||
image = trans_image
|
||||
return image
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
wind_w, wind_h = args.window_size.split('*')
|
||||
wind_w, wind_h = int(wind_w), int(wind_h)
|
||||
wind_w, wind_h = int(wind_w), int(wind_h) # showing windows size
|
||||
cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options,
|
||||
args.phase)
|
||||
|
||||
dataset, pipeline = build_dataset_pipeline(cfg, args.phase)
|
||||
dataset, pipelines = build_dataset_pipelines(cfg, args.phase)
|
||||
CLASSES = dataset.CLASSES
|
||||
display_number = min(args.number, len(dataset))
|
||||
progressBar = ProgressBar(display_number)
|
||||
|
||||
with vis.ImshowInfosContextManager(fig_size=(wind_w, wind_h)) as manager:
|
||||
for i, item in enumerate(itertools.islice(dataset, display_number)):
|
||||
image = get_display_img(item, pipeline, args.mode, args.bgr2rgb)
|
||||
if args.adaptive:
|
||||
image = adaptive_size(args.mode, image, args.min_edge_length,
|
||||
args.max_edge_length)
|
||||
image = get_display_img(args, item, pipelines)
|
||||
|
||||
# dist_path is None as default, means not save pictures
|
||||
# dist_path is None as default, means not saving pictures
|
||||
dist_path = None
|
||||
if args.output_dir:
|
||||
# some datasets do not have filename, such as cifar, use id
|
||||
# some datasets don't have filenames, such as cifar
|
||||
src_path = item.get('filename', '{}.jpg'.format(i))
|
||||
dist_path = os.path.join(args.output_dir, Path(src_path).name)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue