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import argparse
import json
import tqdm
import cv2
import os
import numpy as np
import random
from pycocotools.mask import encode, decode, frPyObjects
EVALMODE = "test"
def fuse_mask(mask_list):
fused_mask = np.zeros_like(mask_list[0])
for mask in mask_list:
fused_mask[mask == 1] = 1
return fused_mask
def blend_mask(input_img, binary_mask, alpha=0.5, color="g"):
if input_img.ndim == 2:
return input_img
mask_image = np.zeros(input_img.shape, np.uint8)
if color == "r":
mask_image[:, :, 0] = 255
if color == "g":
mask_image[:, :, 1] = 255
if color == "b":
mask_image[:, :, 2] = 255
if color == "o":
mask_image[:, :, 0] = 255
mask_image[:, :, 1] = 165
mask_image[:, :, 2] = 0
if color == "c":
mask_image[:, :, 0] = 0
mask_image[:, :, 1] = 255
mask_image[:, :, 2] = 255
if color == "p":
mask_image[:, :, 0] = 128
mask_image[:, :, 1] = 0
mask_image[:, :, 2] = 128
mask_image = mask_image * np.repeat(binary_mask[:, :, np.newaxis], 3, axis=2)
blend_image = input_img[:, :, :].copy()
pos_idx = binary_mask > 0
for ind in range(input_img.ndim):
ch_img1 = input_img[:, :, ind]
ch_img2 = mask_image[:, :, ind]
ch_img3 = blend_image[:, :, ind]
ch_img3[pos_idx] = alpha * ch_img1[pos_idx] + (1 - alpha) * ch_img2[pos_idx]
blend_image[:, :, ind] = ch_img3
return blend_image
def upsample_mask(mask, frame):
H, W = frame.shape[:2]
mH, mW = mask.shape[:2]
if W > H:
ratio = mW / W
h = H * ratio
diff = int((mH - h) // 2)
if diff == 0:
mask = mask
else:
mask = mask[diff:-diff]
else:
ratio = mH / H
w = W * ratio
diff = int((mW - w) // 2)
if diff == 0:
mask = mask
else:
mask = mask[:, diff:-diff]
mask = cv2.resize(mask, (W, H))
return mask
def downsample(mask, frame):
H, W = frame.shape[:2]
mH, mW = mask.shape[:2]
mask = cv2.resize(mask, (W, H))
return mask
#datapath /datasegswap
#inference_path /inference_xmem_ego_last/coco
#output /data/work2-gcp-europe-west4-a/yuqian_fu/Ego/vis_piano
#--show_gt要加上
#视角的切换主要依赖于inference_path是什么
if __name__ == "__main__":
# test_ids = os.listdir(args.datapath)
#修改
color = ['g', 'r', 'b', 'o', 'c', 'p']
filter_byname_path = "/work/yuqian_fu/Ego/filter_takes_byname.json"
split_path = "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/data/split.json"
data_path = "/work/yuqian_fu/Ego/data_segswap"
json_path = "/work/yuqian_fu/Ego/data_segswap/egoexo_val_framelevel_all.json" #debug
#json_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/exoquery_val_framelevel.json"
output_path = "/work/yuqian_fu/Ego/vis_gt_predictions_split_1113"
setting = "ego2exo"
#setting = "exo2ego"
with open(split_path, "r") as fp:
raw_takes = json.load(fp)
with open(json_path, "r") as fp:
datas = json.load(fp)
with open(filter_byname_path, "r") as fp:
take_names = json.load(fp)
#random.seed(0)
#random.shuffle(test_ids)
# takes_ids = raw_takes['val']
#takes_ids = random.sample(takes_ids, 1)
# takes_ids = take_names["soccer"]
# takes_ids = random.sample(takes_ids, 10)
takes_ids = ["3a1b3ec6-13fd-43f4-8af6-f943953e01e4"]
#takes_ids = ["0fe5b647-cdd0-43e9-8710-b33a2e0f83ef", "7c341853-eea3-4243-ab99-e11919aefa4c", "27369696-d356-4d1b-8a22-be8bd4442150", "f291d174-596d-471f-836b-993315197824", "d300ece0-41a7-4707-a08b-2f48aedeb75f", "2fe390a8-1506-4420-9008-74199f92797b"]
for take_id in tqdm.tqdm(takes_ids):
data_list = []
for data in datas:
if data["video_name"] == take_id:
data_list.append(data)
#获取每一个take下的摄像机
data_tmp = data_list[0]
target_cam = data_tmp["image"].split("/")[-2]
query_cam = data_tmp["first_frame_image"].split("/")[-2]
#开始按帧保存fuse-mask
for data in data_list:
name = data["image"].split("/")[-1]
frame_idx = name.split(".")[0]
#target gt
frame_target = cv2.imread(
f"{data_path}/{data['image']}"
)
#debug 提高分辨率
# h,w = frame_target.shape[:2]
# frame_target = cv2.resize(frame_target, (w * 4, h * 4), interpolation=cv2.INTER_CUBIC)
for i,ann in enumerate(data["anns"]):
mask = decode(ann["segmentation"])
mask = downsample(mask, frame_target)
#mask = upsample_mask(mask, frame_target) #debug 提高分辨率
out = blend_mask(frame_target, mask, color=color[0])
os.makedirs(
f"{output_path}/{setting}/bike/{take_id}/gt/obj_{i}/{target_cam}", #debug
exist_ok=True,
)
cv2.imwrite(
f"{output_path}/{setting}/bike/{take_id}/gt/obj_{i}/{target_cam}/{frame_idx}.jpg", #debug
out,
)
#query gt
frame_query = cv2.imread(
f"{data_path}/{data['first_frame_image']}"
)
# h2,w2 = frame_query.shape[:2] #debug: 提高分辨率
# frame_query = cv2.resize(frame_query, (w2 * 2, h2 * 2), interpolation=cv2.INTER_CUBIC)
for i,ann in enumerate(data["first_frame_anns"]):
mask = decode(ann["segmentation"])
mask = downsample(mask, frame_query)
#mask = upsample_mask(mask, frame_query) #debug 提高分辨率
out = blend_mask(frame_query, mask, color=color[0])
os.makedirs(
f"{output_path}/{setting}/bike/{take_id}/gt/obj_{i}/{query_cam}", #debug
exist_ok=True,
)
cv2.imwrite(
f"{output_path}/{setting}/bike/{take_id}/gt/obj_{i}/{query_cam}/{frame_idx}.jpg", #debug
out,
)
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