def t_net(prefix, epoch, batch_size, test_mode, thresh, min_face_size=20,
stride=2, slide_window=False):
detectors = [None, None, None]
# 生成指定模型的困难样本
print("Test model: ", test_mode)
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
# PNet模型
print(model_path[0])
if slide_window:
p_net = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
p_net = FcnDetector(P_Net, model_path[0])
detectors[0] = p_net
# RNet模型
if test_mode in ["RNet", "ONet"]:
print("================= {} =================".format(test_mode))
r_net = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = r_net
# ONet模型，这个模式下生成的样本主要用来观察，而不是训练
if test_mode == "ONet":
print("================== {} ================".format(test_mode))
o_net = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = o_net
# 读取bounding box的ground truth及图片，type:dict，include key 'images' and 'bboxes'
data = read_bboxes_data(path_config.point2_train_txt_path, path_config.images_dir)
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
print('加载原始图片数据，以进行检测及生成困难样本...')
test_data = TestLoader(data['images'])
print('加载完成， 开始检测...')
detections, _ = mtcnn_detector.detect_images(test_data)
print('检测完成！')
# 保存检测结果
if test_mode == "PNet":
save_path = path_config.rnet_save_hard_path
elif test_mode == "RNet":
save_path = path_config.onet_save_hard_path
else:
raise ValueError('网络类型(--test_mode)错误！')
print('保存检测结果的路径为:', save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
print("%s测试完成，开始生成困难样本..." % test_mode)
save_hard_example(data, test_mode, save_path)
class PNetTester(object):
""" 测试PNet模型 """
def __init__(self, models_path, image_path, ground_truth_file):
""" 初始化：加载PNet模型、待测试图片路径及对应ground truth
:param models_path: [PNet, RNet, ONet]模型路径
:param image_path: 待测试图片路径
:param ground_truth_file: 待测图片目标ground_truth文件
"""
# 初始化PNet模型检测器
pnet_model = None
rnet_model = None
onet_model = None
if models_path[0] is not None:
pnet_model = FcnDetector(P_Net, models_path[0])
if models_path[1] is not None:
rnet_model = Detector(R_Net, 24, 256, models_path[1])
if models_path[2] is not None:
onet_model = Detector(O_Net, 48, 16, models_path[2])
self.detector = MtcnnDetector([pnet_model, rnet_model, onet_model], min_face_size=20, stride=2,
threshold=[0.7, 0.7, 0.7], scale_factor=0.79, slide_window=False)
# 初始化ground truth
self.ground_map = dict()
valid_image_path = list()
with open(ground_truth_file, 'r') as truth_file:
for ground_truth in truth_file:
ground_truth = ground_truth.strip().split(' ')
self.ground_map[ground_truth[0]] = np.array([float(_) for _ in ground_truth[1:]])
valid_image_path.append(ground_truth[0])
# 初始化图片加载器
if os.path.isdir(image_path):
images_path = PNetTester.search_file(image_path)
elif os.path.isfile(image_path) and image_path.endswith('.jpg'):
images_path = [image_path]
self.images_path = list()
for image_path in images_path:
if os.path.basename(image_path) in valid_image_path:
self.images_path.append(image_path)
print('待检测图片数量：', len(self.images_path))
self.test_loader = TestLoader(self.images_path)
return
@staticmethod
def search_file(search_path):
"""在指定目录search_path下，递归目录搜索jpg文件
:param search_path: 指定的搜索目录，如：./2018年收集的素材并已校正
:return: 该目录下所有jpg文件的路径组成的list
"""
jpg_path_list = list()
# 获取：1.父目录绝对路径 2.所有文件夹名字（不含路径） 3.所有文件名字
for root_path, dir_names, file_names in os.walk(search_path):
# 收集符合条件的文件名
for filename in file_names:
if filename.endswith('.jpg') and filename.find(' ') == -1:
jpg_path_list.append(os.path.join(root_path, filename))
return jpg_path_list
def test(self, threshold, model_iter):
"""
:param threshold:
:param model_iter:
:return:
"""
all_boxes, landmark = self.detector.detect_images(self.test_loader)
hard_samples = list()
recall = 0 # 召回率：TP / (TP + TN)
acc_pos = 0
acc_all = 0
precision = 0 # 精确率：TP / (TP + FP)
save_path = os.path.join(os.path.dirname(self.images_path[0]), '..', 'result')
if not os.path.exists(save_path):
os.makedirs(save_path)
for index, image_path in enumerate(self.images_path):
image_name = os.path.basename(image_path)
ground_truth = self.ground_map[image_name]
if len(all_boxes[index]) == 0:
print('图片{}检测不到车牌'.format(image_name))
continue
# 计算iou，并画框
iou = np.ones((len(all_boxes[index]),))
gt_boxes = np.array([ground_truth])
for j, box in enumerate(all_boxes[index]):
iou[j] = IoU(box, gt_boxes)
'''
# 画图
im = cv2.imread(image_path)
for j, box in enumerate(all_boxes[index]):
# if image_name == '20180929172716720_23609_dqp001_甘A5T470.jpg':
# pdb.set_trace()
# 绘制iou大于阈值的pos框
if iou[j] > threshold:
cv2.rectangle(im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
(255, 0, 0), 2)
cv2.putText(im, '{:s}|{:.2f}|{:.2f}'.format('p', box[4], iou[j]),
(int(box[0]), int(box[1])), cv2.FONT_HERSHEY_SIMPLEX,
1, (255, 0, 0))
for k in range(5):
cv2.circle(im, (landmark[index][j][2*k], landmark[index][j][2*k+1]), 1, (0, 0, 255), 4)
# 绘制ground truth
cv2.rectangle(im, (int(ground_truth[0]), int(ground_truth[1])),
(int(ground_truth[2]), int(ground_truth[3])),
(0, 255, 0), 2)
cv2.imwrite(os.path.join(save_path, os.path.splitext(image_name)[0] + '_' + model_iter + '.jpg'), im)
print('IoU:\n', iou)
print('average iou = {}'.format(sum(iou) / sum(iou != 0)))
'''
# 计算检测框iou大于阈值的平均精度
if iou.max() > threshold:
recall += 1
acc_pos += np.mean(all_boxes[index][iou > threshold, 4])
acc_all += np.mean(all_boxes[index][:, 4])
precision += len(all_boxes[index][iou > threshold, 4]) / len(all_boxes[index][:, 4])
else:
hard_samples.append(image_path)
precision /= recall
acc_pos /= recall
acc_all /= recall
recall /= self.test_loader.size
print('IoU threshold={}:'.format(threshold), 'precision={},'.format(precision),
' acc-pos={},'.format(acc_pos), 'acc-all={}'.format(acc_all), 'recall={}'.format(recall))
return precision, acc_pos, acc_all, recall
