add a demo to inference a single image (#2605)

demo/image_demo.py

+from argparse import ArgumentParser
+
+from mmdet.apis import inference_detector, init_detector, show_result_pyplot
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    args = parser.parse_args()
+
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+    # test a single image
+    result = inference_detector(model, args.img)
+    # show the results
+    show_result_pyplot(model, args.img, result)
+
+
+if __name__ == '__main__':
+    main()

mmdet/apis/inference.py

@@ -130,12 +130,7 @@ async def async_inference_detector(model, img):
     return result
 
 
-def show_result_pyplot(model,
-                       img,
-                       result,
-                       class_names,
-                       score_thr=0.3,
-                       fig_size=(15, 10)):
+def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)):
     """Visualize the detection results on the image.
 
     Args:
@@ -143,7 +138,6 @@ def show_result_pyplot(model,
         img (str or np.ndarray): Image filename or loaded image.
         result (tuple[list] or list): The detection result, can be either
             (bbox, segm) or just bbox.
-        class_names (list[str] or tuple[str]): A list of class names.
         score_thr (float): The threshold to visualize the bboxes and masks.
         fig_size (tuple): Figure size of the pyplot figure.
     """
@@ -152,3 +146,4 @@ def show_result_pyplot(model,
     img = model.show_result(img, result, score_thr=score_thr, show=False)
     plt.figure(figsize=fig_size)
     plt.imshow(mmcv.bgr2rgb(img))
+    plt.show()