refine wiki

fluid/object_detection/README.md

@@ -1,23 +1,24 @@
-The minimum PaddlePaddle version needed for the code sample in this directory is the lastest develop branch. If you are on a version of PaddlePaddle earlier than this, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
+The minimum PaddlePaddle version needed for the code sample in this directory is the latest develop branch. If you are on a version of PaddlePaddle earlier than this, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
 
 ---
 
 ## SSD Object Detection
 
 ### Introduction
 
-[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection can be categorized as a single stage detector. A single stage detector simplifies object detection as a regression problem, which directly predicts the bounding boxes and class probabilities without region proposal. SSD further makes improves by producing these predictions of different scales from different layers, as shown below. SSD can be applied to a wide variant standard convolutional network for image classification, such as VGG, ResNet, or MobileNet, which is also called base network or backbone. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861).
+[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection can be categorized as a single stage detector. A single stage detector simplifies object detection as a regression problem, which directly predicts the bounding boxes and class probabilities without region proposal. SSD further makes improves by producing these predictions of different scales from different layers, as shown below. SSD is readily pluggable into to a wide variant standard convolutional network, such as VGG, ResNet, or MobileNet, which is also called base network or backbone. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861).
 <p align="center">
 <img src="images/SSD_paper_figure.jpg" height=300 width=900 hspace='10'/> <br />
 The Single Shot MultiBox Detector (SSD)
 </p>
+
 ### Data Preparation
 
 You can use [PASCAL VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/) or [MS-COCO dataset](http://cocodataset.org/#download).
 
 #### PASCAL VOC Dataset
 
-If you want to train model on PASCAL VOC dataset, please download datset at first, skip this step if you already have one.
+If you want to train a model on PASCAL VOC dataset, please download dataset at first, skip this step if you already have one.
 
 ```bash
 cd data/pascalvoc
@@ -28,7 +29,7 @@ The command `download.sh` also will create training and testing file lists.
 
 #### MS-COCO Dataset
 
-If you want to train model on MS-COCO dataset, please download datset at first, skip this step if you already have one.
+If you want to train a model on MS-COCO dataset, please download dataset at first, skip this step if you already have one.
 
 ```
 cd data/coco
@@ -39,9 +40,9 @@ cd data/coco
 
 #### Download the Pre-trained Model.
 
-We provide two pre-trained models. The one is MobileNet-v1 SSD trained on COCO dataset, but removed the convolutional predictors for COCO dataset. This model can be used to initialize the models when training other dataset, like PASCAL VOC. Then other pre-trained model is MobileNet v1 trained on ImageNet 2012 dataset, but removed the last weights and bias in Fully-Connected layer.
+We provide two pre-trained models. The one is MobileNet-v1 SSD trained on COCO dataset, but removed the convolutional predictors for COCO dataset. This model can be used to initialize the models when training other datasets, like PASCAL VOC. The other pre-trained model is MobileNet v1 trained on ImageNet 2012 dataset but removed the last weights and bias in the Fully-Connected layer.
 
-Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md). The MobileNet v1 model is converted [Caffe](https://github.com/shicai/MobileNet-Caffe).
+Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md). The MobileNet v1 model is converted from [Caffe](https://github.com/shicai/MobileNet-Caffe).
 
   - Download MobileNet-v1 SSD:
     ```
@@ -63,7 +64,7 @@ train.py is the main caller of the training module. Examples of usage are shown
 
 ### Evaluate
 
-You can evaluate your trained model in different metric like 11point, integral on both PASCAL VOC and COCO dataset.  Note we set the defualt test list to the dataset's test/val list, you can use your own test list by setting ```--test_list``` args.
+You can evaluate your trained model in different metrics like 11point, integral on both PASCAL VOC and COCO dataset. Note we set the default test list to the dataset's test/val list, you can use your own test list by setting ```--test_list``` args.
 
 eval.py is the main caller of the evaluating module. Examples of usage are shown below.
 ```python
@@ -106,4 +107,3 @@ MobileNet-SSD300x300 Visualization Examples
 |MobileNet-v1-SSD 300x300  | COCO MobileNet SSD | VOC07+12 trainval| VOC07 test   | xx%  |
 |MobileNet-v1-SSD 300x300  | ImageNet MobileNet | VOC07+12 trainval| VOC07 test   | xx%  |
 |MobileNet-v1-SSD 300x300  | ImageNet MobileNet | MS-COCO trainval | MS-COCO test | xx%  |
-

fluid/object_detection/infer.py

@@ -5,6 +5,7 @@
 import functools
 from PIL import Image
 from PIL import ImageDraw
+from PIL import ImageFont
 
 import paddle
 import paddle.fluid as fluid
@@ -20,7 +21,7 @@
 add_arg('image_path',       str,   '',        "The image used to inference and visualize.")
 add_arg('model_dir',        str,   '',     "The model path.")
 add_arg('nms_threshold',    float, 0.45,   "NMS threshold.")
-add_arg('confs_threshold',  float, 0.2,    "Confidence threshold to draw bbox.")
+add_arg('confs_threshold',  float, 0.5,    "Confidence threshold to draw bbox.")
 add_arg('resize_h',         int,   300,    "The resized image height.")
 add_arg('resize_w',         int,   300,    "The resized image height.")
 add_arg('mean_value_B',     float, 127.5,  "Mean value for B channel which will be subtracted.")  #123.68
@@ -58,11 +59,12 @@ def if_exist(var):
                           fetch_list=[nmsed_out],
                           return_numpy=False)
     nmsed_out_v = np.array(nmsed_out_v[0])
-    draw_bounding_box_on_image(image_path, nmsed_out_v,
-                               args.confs_threshold)
+    draw_bounding_box_on_image(image_path, nmsed_out_v, args.confs_threshold,
+                               data_args.label_list)
 
 
-def draw_bounding_box_on_image(image_path, nms_out, confs_threshold):
+def draw_bounding_box_on_image(image_path, nms_out, confs_threshold,
+                               label_list):
     image = Image.open(image_path)
     draw = ImageDraw.Draw(image)
     im_width, im_height = image.size
@@ -80,6 +82,8 @@ def draw_bounding_box_on_image(image_path, nms_out, confs_threshold):
              (left, top)],
             width=4,
             fill='red')
+        if image.mode == 'RGB':
+            draw.text((left, top), label_list[int(category_id)], (255, 255, 0))
     image_name = image_path.split('/')[-1]
     print("image with bbox drawed saved as {}".format(image_name))
     image.save(image_name)