From a4fa39bebc8e67b94a86ebc28126afea0babf154 Mon Sep 17 00:00:00 2001 From: buxingyuan Date: Thu, 7 Jun 2018 15:28:24 +0800 Subject: [PATCH] refine wiki --- fluid/object_detection/README.md | 16 ++++++++-------- fluid/object_detection/images/009943.jpg | Bin 24655 -> 24753 bytes fluid/object_detection/images/009948.jpg | Bin 39409 -> 0 bytes fluid/object_detection/images/009952.jpg | Bin 77186 -> 0 bytes fluid/object_detection/images/009956.jpg | Bin 44173 -> 43279 bytes fluid/object_detection/images/009957.jpg | Bin 38001 -> 0 bytes fluid/object_detection/images/009960.jpg | Bin 33779 -> 33715 bytes fluid/object_detection/images/009962.jpg | Bin 46363 -> 46819 bytes fluid/object_detection/infer.py | 12 ++++++++---- 9 files changed, 16 insertions(+), 12 deletions(-) delete mode 100644 fluid/object_detection/images/009948.jpg delete mode 100644 fluid/object_detection/images/009952.jpg delete mode 100644 fluid/object_detection/images/009957.jpg diff --git a/fluid/object_detection/README.md b/fluid/object_detection/README.md index 9b7f916fcb..2bf0191e75 100644 --- a/fluid/object_detection/README.md +++ b/fluid/object_detection/README.md @@ -1,4 +1,4 @@ -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). --- @@ -6,18 +6,19 @@ The minimum PaddlePaddle version needed for the code sample in this directory is ### 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).


The Single Shot MultiBox Detector (SSD)

+ ### 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% | - diff --git a/fluid/object_detection/images/009943.jpg b/fluid/object_detection/images/009943.jpg index 2ad1d56ecd0d10d2abe6e38b72091a6f31f00bd3..4a1e30f131ca0d52b59be0df8c5326d5f55dc136 100644 GIT binary patch delta 15660 zcmY*G-=*)z6+v`Nyb_zf*YdMX2`1@!14ZZG7KVSDx4XD|SpP9*j80Whar8)z4 zuLRT`sv{S|eyqwHgIjQqBKY<)mQa?@jaRc?b#7@{I=!;#-?{}+4Cqyw)+eUk5Zr?L zII%**G<0D+@e7oxJ{3s|`Ze}#_&ZP3N{f7aZbY1SQ2Au#ju-A0q)c2B zbG16l>a*8)q-ju;GG!B1ArBX7_Vop1f%G+RR~tdpOCJK~)r0|D(y-#LUZG(+z(ey= z1TO|z-kx#np`{-ZXk0dH*%AScEpQ=YU*6k$F62?{=108i-#M>AIX zKbwiJCVL<6vo)S0VAc0jPI9;Sw?jh$Ue51=mAKqn5cBUjRsqq|CK%1*CUvezYN6#= 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z?xi~U#x3u*fKid}u9mnRlXEq^Nwrrd4zcdXaH!7!;<{X0KNtiQs~eOC>25qR*12IX zhEQSmd#;}~%z|+4#_G*(2VVC{O~OnYH8rHNl|2Z(jeeynMgJ&w;55%@kw$AnKJ`eJ(0V7xO-XROeWwwcWWlXw z{3humO0K44XLl~Wp=KQ#3OEAQOuFw)qs96AN-=O<`D6ng!zqE2kL9h2#$QsXwc-We zcV(`|Sm(w7r8d(PdA*tajiaf;9}kVk1iN`TaDp`-Fp=#|9GBL-D3{(6{{Q&A0^W*Fwk~O!m QK&QunB}bw->)+h}0^hN-ivR!s diff --git a/fluid/object_detection/infer.py b/fluid/object_detection/infer.py index ab55ad6b8a..8117a9727d 100644 --- a/fluid/object_detection/infer.py +++ b/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)