def get_acc_pre_rec(model, points=100):
acc_list = []
threshold_list = []
precision_list = []
recall_list = []
for SCORE_THRESHOLD in tqdm.tqdm(range(1, points)):
ts.test(model, SCORE_THRESHOLD / points)
main_map.get_map()
threshold_list.append(float(SCORE_THRESHOLD / points))
# 得到#号的行
with open(results_path, 'r') as results_f:
results_data = results_f.readlines()
indexes = []
for i, line in enumerate(results_data):
if line.find('#') > -1:
indexes.append(i)
#统计gt的个数
gt_obj_name_all = {}
for i in range(len(results_data)):
if i > indexes[2] and i < (indexes[3] - 1):
d = results_data[i].strip().replace(' ', '').split(':')
gt_obj_name_all[d[0]] = int(d[1])
if i >= indexes[3]: break
gt_num_list = list(gt_obj_name_all.values())
gt_num = np.sum(gt_num_list)
# print('测试集中的所有样本数据：', obj_name_all)
# 读取fp，fp；计算R-P值
predict_correct = 0
predict_sum = 0
for d in results_data:
if d.find('tp') > -1:
d_ = d.strip().replace(' ', '').replace('(', '#').replace(
',', '#').replace(')', '').split('#')
d_0 = d_[0].split(':')
d_1 = d_[1].split(':')
predict_sum += int(d_0[1])
predict_correct += int(d_1[1])
acc = float(predict_correct / gt_num)
precision = float(predict_correct / predict_sum)
recall = float(predict_correct / gt_num)
acc_list.append(acc)
precision_list.append(precision)
recall_list.append(recall)
return threshold_list, acc_list, precision_list, recall_list
def on_epoch_end(self, epoch, logs={}):
self.count_epochs += 1
if self.count_epochs % 5 == 0:
test(self.model, 0.3, draw_boxes=False)
mAp = main_map.get_map()
print(mAp)
self.mAP_list.append(mAp)
with open(self.map_list_save_path, 'wb') as map_f:
pickle.dump(self.mAP_list, map_f)
plt.ylabel('Recall')
plt.axis([0, 1, 0, 1])
plt.plot(threshold_list, recall_list, 'r-')
plt.savefig(pre_fig_path)
if __name__ == '__main__':
create = True
if create:
model = create_model()
weights_path = os.path.join(CurDir, 'model', "yolov3.weights")
model.load_weights(weights_path)
model.summary()
ts.test(model, 0.3, draw_boxes=False)
print(get_map())
plot_class_precision()
'''
plot_map()
plot_train_loss()
plot_test_loss()
ts.test(model, 0.3, draw_boxes=True)
print(get_map())
plot_class_precision()
sumpr()
threshold_list, acc_list, precision_list, recall_list = get_acc_pre_rec(model, points=10)
plot_acc(threshold_list, acc_list)
plot_precision(threshold_list, precision_list)
plot_recall(threshold_list, recall_list)
'''