plot_occlusions.py

import os
import json
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from PIL import Image

# 设置全局字体为 Times New Roman
plt.rcParams['font.family'] = 'serif'

# 加载 JSON 数据
json_path = "/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/video_exp.json"
with open(json_path, 'r') as f:
    json_dict = json.load(f)["videos"]

# 获取每个视频的帧数
gt_mask_path = "/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/Annotations"
occlusion_list = []
change_time_list = []
occlusion_frame_list = []
num_valid_masks = 0
for vid_name, video_dict in json_dict.items():
    new_obj_id = 0    # 该视频中实例的数量
    object_list = []
    for exp_id, exp_dict in video_dict["expressions"].items():
        object_list.extend(exp_dict["obj_id"])
        # for obj_id in exp_dict["obj_id"]:
        #     if int(obj_id) != new_obj_id:
        #         new_obj_id = int(obj_id)
        #         object_list.append(obj_id)
    vid_frames = video_dict["frames"]   
    object_list = list(set(object_list))
    # obj_ids = list(range(num_object))  
    if "LongVOS" in vid_name:
        for obj_id in object_list:
            total_frames = len(vid_frames)
            num_occlusion_frames = 0
            per_obj_change_time_list = []
            for frame_idx, frame_name in enumerate(vid_frames):
                # processing the gt mask
                gt_mask_ = np.array(Image.open(os.path.join(gt_mask_path, vid_name, frame_name + '.png')))
                tmp_masks = np.zeros_like(gt_mask_)
                
                tmp_masks[gt_mask_ == int(obj_id)] = 1
                gt_mask = tmp_masks
                gt_mask[gt_mask == 255] = 0
                
                if np.all(gt_mask == 0):
                    num_occlusion_frames += 1
                    per_obj_change_time_list.append(0)
                else:
                    per_obj_change_time_list.append(1)
                    num_valid_masks += 1
            
            change_time_list.append(int(np.count_nonzero(np.diff(per_obj_change_time_list))))
            occlusion_list.append(num_occlusion_frames / total_frames)
            occlusion_frame_list.append(num_occlusion_frames)
                
    else:
        anno_path = os.path.join(gt_mask_path, vid_name)
        mask_list = [os.path.join(anno_path, d) for d in os.listdir(anno_path)]
        mask_list.sort()
        for obj_id in object_list:
            mask_path = mask_list[int(obj_id) - 1]    
            mask_path_list = sorted(os.listdir(mask_path))
            total_frames = len(mask_path_list)
            per_obj_change_time_list = []
            num_occlusion_frames = 0
            for i, mask_path_ in enumerate(mask_path_list):
                mask_path_ = os.path.join(mask_path, mask_path_)
                mask = np.array(Image.open(mask_path_)) > 0
                if np.all(mask == 0) :
                    num_occlusion_frames += 1
                    per_obj_change_time_list.append(0)
                else:
                    num_valid_masks += 1
                    per_obj_change_time_list.append(1)
                    
            change_time_list.append(int(np.count_nonzero(np.diff(per_obj_change_time_list))))
            occlusion_list.append(num_occlusion_frames / total_frames)
            occlusion_frame_list.append(num_occlusion_frames)
            
print(occlusion_list)
print(change_time_list)
print(occlusion_frame_list)
print("num_valid_masks: {}".format(num_valid_masks))

with open("/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/occlusion_list.txt", "w", encoding="utf-8") as f:
    for item in occlusion_list:
        f.write(f"{item}\n")
print("List has been saved to occlusion_list.txt")

with open("/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/change_time_list.txt", "w", encoding="utf-8") as f:
    for item in change_time_list:
        f.write(f"{item}\n")
print("List has been saved to change_time_list.txt")

with open("/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/occlusion_frame_list.txt", "w", encoding="utf-8") as f:
    for item in occlusion_frame_list:
        f.write(f"{item}\n")
print("List has been saved to occlusion_frame_list.txt")

# bins = np.arange(0, 1.1, 0.1)  # 生成 [0.0, 0.1, ..., 1.0]

# # 绘制直方图
# plt.figure(figsize=(6, 4))
# plt.hist(occlusion_list, bins=bins, color=(127 / 255, 255 / 255, 0), edgecolor='black')

# # 设置标签与标题
# plt.xlabel("Occlusion Ratio")
# plt.ylabel("Instance Count")
# plt.title("Occlusion Ratio Distribution (bin=0.1)")
# plt.xticks(bins)  # 设置刻度更精确
# plt.tight_layout()

# # 保存图像
# plt.savefig("occlusion_ratio_distribution.png", dpi=300)
# plt.close()