diff --git a/fluid/PaddleCV/rcnn/README_cn.md b/fluid/PaddleCV/rcnn/README_cn.md index 57e622e60f049d2976038bddb4cb39bd8fbf9756..b7577a858edc135a23ad6d9c3252318e9847aca5 100644 --- a/fluid/PaddleCV/rcnn/README_cn.md +++ b/fluid/PaddleCV/rcnn/README_cn.md @@ -50,9 +50,8 @@ Mask RCNN同样为两阶段框架，第一阶段扫描图像生成候选框； 训练前需要首先下载[cocoapi](https://github.com/cocodataset/cocoapi)： - # COCOAPI=/path/to/clone/cocoapi - git clone https://github.com/cocodataset/cocoapi.git $COCOAPI - cd $COCOAPI/PythonAPI + git clone https://github.com/cocodataset/cocoapi.git + cd cocoapi/PythonAPI # if cython is not installed pip install Cython # Install into global site-packages @@ -65,25 +64,29 @@ Mask RCNN同样为两阶段框架，第一阶段扫描图像生成候选框； - Faster RCNN + ``` python train.py \ --model_save_dir=output/ \ --pretrained_model=${path_to_pretrain_model} \ --data_dir=${path_to_data} \ --MASK_ON=False + ``` - Mask RCNN + ``` python train.py \ --model_save_dir=output/ \ --pretrained_model=${path_to_pretrain_model} \ --data_dir=${path_to_data} \ --MASK_ON=True + ``` -- 通过设置export CUDA\_VISIBLE\_DEVICES=0,1,2,3,4,5,6,7指定8卡GPU训练。 -- 通过设置```MASK_ON```选择Faster RCNN和Mask RCNN模型。 -- 可选参数见： + - 通过设置export CUDA\_VISIBLE\_DEVICES=0,1,2,3,4,5,6,7指定8卡GPU训练。 + - 通过设置```MASK_ON```选择Faster RCNN和Mask RCNN模型。 + - 可选参数见： - python train.py --help + python train.py --help **数据读取器说明：** 数据读取器定义在reader.py中。所有图像将短边等比例缩放至`scales`，若长边大于`max_size`, 则再次将长边等比例缩放至`max_size`。在训练阶段，对图像采用水平翻转。支持将同一个batch内的图像padding为相同尺寸。 @@ -110,20 +113,25 @@ Mask RCNN同样为两阶段框架，第一阶段扫描图像生成候选框； - Faster RCNN + ``` python eval_coco_map.py \ --dataset=coco2017 \ - --pretrained_model=${path_to_pretrain_model} \ + --pretrained_model=${path_to_trained_model} \ --MASK_ON=False + ``` - Mask RCNN + ``` python eval_coco_map.py \ --dataset=coco2017 \ - --pretrained_model=${path_to_pretrain_model} \ + --pretrained_model=${path_to_trained_model} \ --MASK_ON=True + ``` -- 通过设置export CUDA\_VISIBLE\_DEVICES=0指定单卡GPU评估。 -- 通过设置```MASK_ON```选择Faster RCNN和Mask RCNN模型。 + - 通过设置`--pretrained_model=${path_to_trained_model}`指定训练好的模型，注意不是初始化的模型。 + - 通过设置`export CUDA\_VISIBLE\_DEVICES=0`指定单卡GPU评估。 + - 通过设置```MASK_ON```选择Faster RCNN和Mask RCNN模型。 下表为模型评估结果： @@ -155,12 +163,14 @@ Mask RCNN: 模型推断可以获取图像中的物体及其对应的类别，`infer.py`是主要执行程序，调用示例如下： - python infer.py \ - --dataset=coco2017 \ - --pretrained_model=${path_to_pretrain_model} \ - --image_path=dataset/coco/val2017/ \ - --image_name=000000000139.jpg \ - --draw_threshold=0.6 +``` +python infer.py \ + --pretrained_model=${path_to_trained_model} \ + --image_path=dataset/coco/val2017/000000000139.jpg \ + --draw_threshold=0.6 +``` + +注意，请正确设置`${path_to_trained_model}`模型和预测图片的路径。 下图为模型可视化预测结果：
diff --git a/fluid/PaddleCV/rcnn/data_utils.py b/fluid/PaddleCV/rcnn/data_utils.py index 4d63b62646f199cfae6d151781aed40786fcad0d..86be7f1d49762c7e57180304edfde0810374449d 100644 --- a/fluid/PaddleCV/rcnn/data_utils.py +++ b/fluid/PaddleCV/rcnn/data_utils.py @@ -31,6 +31,23 @@ from config import cfg import os +class DatasetPath(object): + def __init__(self, mode): + self.mode = mode + mode_name = 'train' if mode == 'train' else 'val' + if cfg.dataset != 'coco2014' and cfg.dataset != 'coco2017': + raise NotImplementedError('Dataset {} not supported'.format( + cfg.dataset)) + self.sub_name = mode_name + cfg.dataset[-4:] + + def get_data_dir(self): + return os.path.join(cfg.data_dir, self.sub_name) + + def get_file_list(self): + sfile_list = 'annotations/instances_' + self.sub_name + '.json' + return os.path.join(cfg.data_dir, sfile_list) + + def get_image_blob(roidb, mode): """Builds an input blob from the images in the roidb at the specified scales. diff --git a/fluid/PaddleCV/rcnn/eval_coco_map.py b/fluid/PaddleCV/rcnn/eval_coco_map.py index f3671fa25124040fbb674354c3e6cbbf5231988c..06ae3589065d849fda461bc41fa0abf4abd94515 100644 --- a/fluid/PaddleCV/rcnn/eval_coco_map.py +++ b/fluid/PaddleCV/rcnn/eval_coco_map.py @@ -29,7 +29,7 @@ import json from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval, Params from config import cfg -from roidbs import DatasetPath +from data_utils import DatasetPath def eval(): diff --git a/fluid/PaddleCV/rcnn/eval_helper.py b/fluid/PaddleCV/rcnn/eval_helper.py index 92f76e68ea697ac2fb987ddb10c815a82d746020..dba67f6bbed2c87b5794dcc9c01a36205381e0d1 100644 --- a/fluid/PaddleCV/rcnn/eval_helper.py +++ b/fluid/PaddleCV/rcnn/eval_helper.py @@ -199,8 +199,7 @@ def get_segms_res(batch_size, lod, segms_out, data, num_id_to_cat_id_map): def draw_bounding_box_on_image(image_path, nms_out, draw_threshold, - label_list, - num_id_to_cat_id_map, + labels_map, image=None): if image is None: image = Image.open(image_path) @@ -209,7 +208,6 @@ def draw_bounding_box_on_image(image_path, for dt in np.array(nms_out): num_id, score, xmin, ymin, xmax, ymax = dt.tolist() - category_id = num_id_to_cat_id_map[num_id] if score < draw_threshold: continue draw.line( @@ -218,7 +216,7 @@ def draw_bounding_box_on_image(image_path, width=2, fill='red') if image.mode == 'RGB': - draw.text((xmin, ymin), label_list[int(category_id)], (255, 255, 0)) + draw.text((xmin, ymin), labels_map[num_id], (255, 255, 0)) image_name = image_path.split('/')[-1] print("image with bbox drawed saved as {}".format(image_name)) image.save(image_name) @@ -299,3 +297,90 @@ def segm_results(im_results, masks, im_info): segms_results = np.vstack([segms_results[k] for k in range(len(lod) - 1)]) im_results = np.hstack([segms_results, im_results]) return im_results[:, :3] + + +def coco17_labels(): + labels_map = { + 0: 'background', + 1: 'person', + 2: 'bicycle', + 3: 'car', + 4: 'motorcycle', + 5: 'airplane', + 6: 'bus', + 7: 'train', + 8: 'truck', + 9: 'boat', + 10: 'traffic light', + 11: 'fire hydrant', + 12: 'stop sign', + 13: 'parking meter', + 14: 'bench', + 15: 'bird', + 16: 'cat', + 17: 'dog', + 18: 'horse', + 19: 'sheep', + 20: 'cow', + 21: 'elephant', + 22: 'bear', + 23: 'zebra', + 24: 'giraffe', + 25: 'backpack', + 26: 'umbrella', + 27: 'handbag', + 28: 'tie', + 29: 'suitcase', + 30: 'frisbee', + 31: 'skis', + 32: 'snowboard', + 33: 'sports ball', + 34: 'kite', + 35: 'baseball bat', + 36: 'baseball glove', + 37: 'skateboard', + 38: 'surfboard', + 39: 'tennis racket', + 40: 'bottle', + 41: 'wine glass', + 42: 'cup', + 43: 'fork', + 44: 'knife', + 45: 'spoon', + 46: 'bowl', + 47: 'banana', + 48: 'apple', + 49: 'sandwich', + 50: 'orange', + 51: 'broccoli', + 52: 'carrot', + 53: 'hot dog', + 54: 'pizza', + 55: 'donut', + 56: 'cake', + 57: 'chair', + 58: 'couch', + 59: 'potted plant', + 60: 'bed', + 61: 'dining table', + 62: 'toilet', + 63: 'tv', + 64: 'laptop', + 65: 'mouse', + 66: 'remote', + 67: 'keyboard', + 68: 'cell phone', + 69: 'microwave', + 70: 'oven', + 71: 'toaster', + 72: 'sink', + 73: 'refrigerator', + 74: 'book', + 75: 'clock', + 76: 'vase', + 77: 'scissors', + 78: 'teddy bear', + 79: 'hair drier', + 80: 'toothbrush' + } + return labels_map diff --git a/fluid/PaddleCV/rcnn/infer.py b/fluid/PaddleCV/rcnn/infer.py index c9e0598c54162f9418b76ff360f67f715f9dfed6..416d51caf71c7cb0f3860ad3397528d327128842 100644 --- a/fluid/PaddleCV/rcnn/infer.py +++ b/fluid/PaddleCV/rcnn/infer.py @@ -8,26 +8,36 @@ import reader from utility import print_arguments, parse_args import models.model_builder as model_builder import models.resnet as resnet -import json -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval, Params from config import cfg -from roidbs import DatasetPath +from data_utils import DatasetPath def infer(): - data_path = DatasetPath('val') - test_list = data_path.get_file_list() + try: + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval, Params + + data_path = DatasetPath('val') + test_list = data_path.get_file_list() + coco_api = COCO(test_list) + cid = coco_api.getCatIds() + cat_id_to_num_id_map = { + v: i + 1 + for i, v in enumerate(coco_api.getCatIds()) + } + category_ids = coco_api.getCatIds() + labels_map = { + cat_id_to_num_id_map[item['id']]: item['name'] + for item in coco_api.loadCats(category_ids) + } + labels_map[0] = 'background' + except: + print("The COCO dataset is not exist, use the defalut mapping of class " + "index and real category name on COCO17.") + assert cfg.dataset == 'coco2017' + labels_map = coco17_labels() - cocoGt = COCO(test_list) - num_id_to_cat_id_map = {i + 1: v for i, v in enumerate(cocoGt.getCatIds())} - category_ids = cocoGt.getCatIds() - label_list = { - item['id']: item['name'] - for item in cocoGt.loadCats(category_ids) - } - label_list[0] = ['background'] image_shape = [3, cfg.TEST.max_size, cfg.TEST.max_size] class_nums = cfg.class_num @@ -43,12 +53,14 @@ def infer(): place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) # yapf: disable - if cfg.pretrained_model: - def if_exist(var): - return os.path.exists(os.path.join(cfg.pretrained_model, var.name)) - fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist) + if not os.path.exists(cfg.pretrained_model): + raise ValueError("Model path [%s] does not exist." % (cfg.pretrained_model)) + + def if_exist(var): + return os.path.exists(os.path.join(cfg.pretrained_model, var.name)) + fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist) # yapf: enable - infer_reader = reader.infer() + infer_reader = reader.infer(cfg.image_path) feeder = fluid.DataFeeder(place=place, feed_list=model.feeds()) dts_res = [] @@ -67,14 +79,14 @@ def infer(): masks_v = result[1] new_lod = pred_boxes_v.lod() nmsed_out = pred_boxes_v - path = os.path.join(cfg.image_path, cfg.image_name) image = None if cfg.MASK_ON: segms_out = segm_results(nmsed_out, masks_v, im_info) - image = draw_mask_on_image(path, segms_out, cfg.draw_threshold) + image = draw_mask_on_image(cfg.image_path, segms_out, + cfg.draw_threshold) - draw_bounding_box_on_image(path, nmsed_out, cfg.draw_threshold, label_list, - num_id_to_cat_id_map, image) + draw_bounding_box_on_image(cfg.image_path, nmsed_out, cfg.draw_threshold, + labels_map, image) if __name__ == '__main__': diff --git a/fluid/PaddleCV/rcnn/reader.py b/fluid/PaddleCV/rcnn/reader.py index a1c7373bf1495b3f31973b3b7a5bbdfb6830d04c..d2d5abbca8f0175fb1d1ba50ffcd62b2b5ed31da 100644 --- a/fluid/PaddleCV/rcnn/reader.py +++ b/fluid/PaddleCV/rcnn/reader.py @@ -12,16 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -from paddle.utils.image_util import * import random -from PIL import Image -from PIL import ImageDraw import numpy as np import xml.etree.ElementTree import os import time import copy import six +import cv2 from collections import deque from roidbs import JsonDataset @@ -36,8 +34,6 @@ def roidb_reader(roidb, mode): im_height = np.round(roidb['height'] * im_scales) im_width = np.round(roidb['width'] * im_scales) im_info = np.array([im_height, im_width, im_scales], dtype=np.float32) - if mode == 'infer': - return im, im_info if mode == 'val': return im, im_info, im_id @@ -76,11 +72,8 @@ def coco(mode, total_batch_size=None, padding_total=False, shuffle=False): - cfg.mean_value = np.array(cfg.pixel_means)[np.newaxis, - np.newaxis, :].astype('float32') total_batch_size = total_batch_size if total_batch_size else batch_size - if mode != 'infer': - assert total_batch_size % batch_size == 0 + assert total_batch_size % batch_size == 0 json_dataset = JsonDataset(mode) roidbs = json_dataset.get_roidb() @@ -160,14 +153,6 @@ def coco(mode, if len(batch_out) != 0: yield batch_out - else: - for roidb in roidbs: - if cfg.image_name not in roidb['image']: - continue - im, im_info = roidb_reader(roidb, mode) - batch_out = [(im, im_info)] - yield batch_out - return reader @@ -180,5 +165,17 @@ def test(batch_size, total_batch_size=None, padding_total=False): return coco('val', batch_size, total_batch_size, shuffle=False) -def infer(): - return coco('infer') +def infer(file_path): + def reader(): + if not os.path.exists(file_path): + raise ValueError("Image path [%s] does not exist." % (file_path)) + im = cv2.imread(file_path) + im = im.astype(np.float32, copy=False) + im -= cfg.pixel_means + im_height, im_width, channel = im.shape + channel_swap = (2, 0, 1) #(channel, height, width) + im = im.transpose(channel_swap) + im_info = np.array([im_height, im_width, 1.0], dtype=np.float32) + yield [(im, im_info)] + + return reader diff --git a/fluid/PaddleCV/rcnn/roidbs.py b/fluid/PaddleCV/rcnn/roidbs.py index bd7e581999e6e7052238cadc7472b852ec88dab8..6b5386161c3d270c3eb93e60b579727dc397e535 100644 --- a/fluid/PaddleCV/rcnn/roidbs.py +++ b/fluid/PaddleCV/rcnn/roidbs.py @@ -38,27 +38,11 @@ from pycocotools.coco import COCO import box_utils import segm_utils from config import cfg +from data_utils import DatasetPath logger = logging.getLogger(__name__) -class DatasetPath(object): - def __init__(self, mode): - self.mode = mode - mode_name = 'train' if mode == 'train' else 'val' - if cfg.dataset != 'coco2014' and cfg.dataset != 'coco2017': - raise NotImplementedError('Dataset {} not supported'.format( - cfg.dataset)) - self.sub_name = mode_name + cfg.dataset[-4:] - - def get_data_dir(self): - return os.path.join(cfg.data_dir, self.sub_name) - - def get_file_list(self): - sfile_list = 'annotations/instances_' + self.sub_name + '.json' - return os.path.join(cfg.data_dir, sfile_list) - - class JsonDataset(object): """A class representing a COCO json dataset.""" diff --git a/fluid/PaddleCV/rcnn/utility.py b/fluid/PaddleCV/rcnn/utility.py index 7948bc13fb9c540a92603bca8f423d02aecf81c6..fcad5566fd082904c37953b09918f2bf5e0aa273 100644 --- a/fluid/PaddleCV/rcnn/utility.py +++ b/fluid/PaddleCV/rcnn/utility.py @@ -161,7 +161,6 @@ def parse_args(): # SINGLE EVAL AND DRAW add_arg('draw_threshold', float, 0.8, "Confidence threshold to draw bbox.") add_arg('image_path', str, 'dataset/coco/val2017', "The image path used to inference and visualize.") - add_arg('image_name', str, '', "The single image used to inference and visualize.") # ce parser.add_argument( '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')