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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
if __name__ == "__main__":
color = ['g', 'r', 'b', 'o', 'c', 'p']
data_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_dataset_whisks/train"
json_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_dataset_whisks/handal_dataset_whisks_test100.json" #debug
output_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_dataset_whisks/vis"
mask_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_dataset_whisks/mask_predictions/train"
with open(json_path, "r") as fp:
datas = json.load(fp)
takes_ids = os.listdir(mask_path)
for take_id in tqdm.tqdm(takes_ids):
data_list = []
for data in datas:
if data["video_name"] == take_id:
data_list.append(data)
#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']}"
)
for i,ann in enumerate(data["anns"]):
mask = decode(ann["segmentation"])
mask = downsample(mask, frame_target)
out = blend_mask(frame_target, mask, color=color[0])
os.makedirs(
f"{output_path}/{take_id}/obj_{i}/target_gt", #debug
exist_ok=True,
)
cv2.imwrite(
f"{output_path}/{take_id}/obj_{i}/target_gt/{frame_idx}.jpg", #debug
out,
)
#query gt
frame_query = cv2.imread(
f"{data_path}/{data['first_frame_image']}"
)
for i,ann in enumerate(data["first_frame_anns"]):
mask = decode(ann["segmentation"])
mask = downsample(mask, frame_query)
out = blend_mask(frame_query, mask, color=color[0])
os.makedirs(
f"{output_path}/{take_id}/obj_{i}/query_gt", #debug
exist_ok=True,
)
cv2.imwrite(
f"{output_path}/{take_id}/obj_{i}/query_gt/{frame_idx}.jpg", #debug
out,
)
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