Add feature for image demo.

demo/image_demo.py

@@ -1,26 +1,34 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os
+import urllib
 from argparse import ArgumentParser
 
 import mmcv
+import torch
 from mmdet.apis import inference_detector, init_detector
+from mmengine.logging import print_log
+from mmengine.utils import ProgressBar, scandir
 
 from mmyolo.registry import VISUALIZERS
 from mmyolo.utils import register_all_modules
 
+IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
+                  '.tiff', '.webp')
+
 
 def parse_args():
     parser = ArgumentParser()
-    parser.add_argument('img', help='Image file')
+    parser.add_argument(
+        'img', help='Image path, include image file, dir and URL.')
     parser.add_argument('config', help='Config file')
     parser.add_argument('checkpoint', help='Checkpoint file')
-    parser.add_argument('--out-file', default=None, help='Path to output file')
+    parser.add_argument(
+        '--out-dir', default='./output', help='Path to output file')
     parser.add_argument(
         '--device', default='cuda:0', help='Device used for inference')
     parser.add_argument(
-        '--palette',
-        default='coco',
-        choices=['coco', 'voc', 'citys', 'random'],
-        help='Color palette used for visualization')
+        '--show', action='store_true', help='Show the detection results')
     parser.add_argument(
         '--score-thr', type=float, default=0.3, help='bbox score threshold')
     args = parser.parse_args()
@@ -31,29 +39,59 @@ def main(args):
     # register all modules in mmdet into the registries
     register_all_modules()
 
-    # TODO: Support inference of image directory.
     # build the model from a config file and a checkpoint file
     model = init_detector(args.config, args.checkpoint, device=args.device)
 
     # init visualizer
     visualizer = VISUALIZERS.build(model.cfg.visualizer)
     visualizer.dataset_meta = model.dataset_meta
 
-    # test a single image
-    result = inference_detector(model, args.img)
-
-    # show the results
-    img = mmcv.imread(args.img)
-    img = mmcv.imconvert(img, 'bgr', 'rgb')
-    visualizer.add_datasample(
-        'result',
-        img,
-        data_sample=result,
-        draw_gt=False,
-        show=True,
-        wait_time=0,
-        out_file=args.out_file,
-        pred_score_thr=args.score_thr)
+    is_dir = os.path.isdir(args.img)
+    is_url = args.img.startswith(('http:/', 'https:/'))
+    is_file = os.path.splitext(args.img)[-1] in (IMG_EXTENSIONS)
+
+    files = []
+    if is_dir:
+        # when input source is dir
+        for file in scandir(args.img, IMG_EXTENSIONS, recursive=True):
+            files.append(os.path.join(args.img, file))
+    elif is_url:
+        # when input source is url
+        filename = os.path.basename(
+            urllib.parse.unquote(args.img).split('?')[0])
+        torch.hub.download_url_to_file(args.img, filename)
+        files = [os.path.join(os.getcwd(), filename)]
+    elif is_file:
+        # when input source is single image
+        files = [args.img]
+    else:
+        print_log(
+            'Cannot find image file.', logger='current', level=logging.WARNING)
+
+    # start detector inference
+    progress_bar = ProgressBar(len(files))
+    for file in files:
+        result = inference_detector(model, file)
+        img = mmcv.imread(file)
+        img = mmcv.imconvert(img, 'bgr', 'rgb')
+        if is_dir:
+            filename = os.path.relpath(file, args.img).replace('/', '_')
+        else:
+            filename = os.path.basename(file)
+        out_file = None if args.show else os.path.join(args.out_dir, filename)
+        visualizer.add_datasample(
+            filename,
+            img,
+            data_sample=result,
+            draw_gt=False,
+            show=args.show,
+            wait_time=0,
+            out_file=out_file,
+            pred_score_thr=args.score_thr)
+        progress_bar.update()
+    if not args.show:
+        print_log(
+            f'\nResults have been saved at {os.path.abspath(args.out_dir)}')
 
 
 if __name__ == '__main__':

docs/en/get_started.md

@@ -99,10 +99,24 @@ The downloading will take several seconds or more, depending on your network env
 Option (a). If you install MMYOLO from source, just run the following command.
 
 ```shell
-python demo/image_demo.py demo/demo.jpg yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth --device cpu --out-file result.jpg
+python demo/image_demo.py demo/demo.jpg \
+                          yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py \
+                          yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth
+
+# Optional parameters
+# --out-dir ./output *The detection results are output to the specified directory. When args have action --show, the script do not save results. Default: ./output
+# --device cuda:0    *The computing resources used, including cuda and cpu. Default: cuda:0
+# --show             *Display the results on the screen. Default: False
+# --score-thr 0.3    *Confidence threshold. Default: 0.3
 ```
 
-You will see a new image `result.jpg` on your current folder, where bounding boxes are plotted.
+You will see a new image on your `output` folder, where bounding boxes are plotted.
+
+Supported input types:
+
+- Single image, include `jpg`, `jpeg`, `png`, `ppm`, `bmp`, `pgm`, `tif`, `tiff`, `webp`.
+- Folder, all image files in the folder will be traversed and the corresponding results will be output.
+- URL, will automatically download from the URL and the corresponding results will be output.
 
 Option (b). If you install MMYOLO with MIM, open your python interpreter and copy&paste the following codes.
 

docs/zh_cn/get_started.md

@@ -100,12 +100,22 @@ mim download mmyolo --config yolov5_s-v61_syncbn_fast_8xb16-300e_coco --dest .
 ```shell
 python demo/image_demo.py demo/demo.jpg \
                           yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py \
-                          yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth \
-                          --device cpu \
-                          --out-file result.jpg
+                          yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth
+
+# 可选参数
+# --out-dir ./output *检测结果输出到指定目录下，默认为./output, 当--show参数存在时，不保存检测结果
+# --device cuda:0    *使用的计算资源，包括cuda, cpu等，默认为cuda:0
+# --show             *使用该参数表示在屏幕上显示检测结果，默认为False
+# --score-thr 0.3    *置信度阈值，默认为0.3
 ```
 
-你会在当前文件夹中看到一个新的图像 `result.jpg`，图像中包含有网络预测的检测框。
+运行结束后，在 `output` 文件夹中可以看到检测结果图像，图像中包含有网络预测的检测框。
+
+支持输入类型包括
+
+- 单张图片, 支持 `jpg`, `jpeg`, `png`, `ppm`, `bmp`, `pgm`, `tif`, `tiff`, `webp`。
+- 文件目录，会遍历文件目录下所有图片文件，并输出对应结果。
+- 网址，会自动从对应网址下载图片，并输出结果。
 
 方案 2. 如果你通过 MIM 安装的 MMYOLO， 那么可以打开你的 Python 解析器，复制并粘贴以下代码：
 

mmyolo/__init__.py

@@ -11,7 +11,7 @@
 mmcv_version = digit_version(mmcv.__version__)
 
 mmengine_minimum_version = '0.1.0'
-mmengine_maximum_version = '0.2.0'
+mmengine_maximum_version = '0.2.1'
 mmengine_version = digit_version(mmengine.__version__)
 
 mmdet_minimum_version = '3.0.0rc1'