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import argparse
import json
import tqdm
import cv2
import os
import numpy as np
from pycocotools import mask as mask_utils
import random
from PIL import Image
from natsort import natsorted
from pycocotools.mask import encode, decode, frPyObjects
EVALMODE = "test"
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
if color == "l":
mask_image[:, :, 0] = 128
mask_image[:, :, 1] = 128
mask_image[:, :, 2] = 0
if color == "m":
mask_image[:, :, 0] = 128
mask_image[:, :, 1] = 128
mask_image[:, :, 2] = 128
if color == "q":
mask_image[:, :, 0] = 165
mask_image[:, :, 1] = 80
mask_image[:, :, 2] = 30
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 /vis_piano
#--show_gt要加上
if __name__ == "__main__":
color = ['g', 'r', 'b', 'o', 'c', 'p', 'l', 'm', 'q']
data_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL"
output_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/Handal_vis_results_correct_last"
mask_base_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/predictions_handal_all"
json_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_test_all_instruct_correct_videoname.json"
with open(json_path, "r") as fp:
datas = json.load(fp)
print(len(datas))
video_select = ["handal_dataset_fixed_joint_pliers"] # debug
for video_name in tqdm.tqdm(video_select):
#实验需改动
data_list = []
for data in datas:
if data["video_name"] == video_name:
data_list.append(data)
print(len(data_list))
data_list = random.sample(data_list, 100)
for data in data_list:
query_img = cv2.imread(os.path.join(data_path, data['first_frame_image']))
target_img = cv2.imread(os.path.join(data_path, data['image']))
# target_gt
for i,ann in enumerate(data["anns"]):
mask = decode(ann["segmentation"])
mask = downsample(mask, target_img)
out = blend_mask(target_img, mask, color=color[0])
os.makedirs(
f"{output_path}/{video_name}/target_gt", #debug
exist_ok=True,
)
img_path1 = data['image']
tmp_list = img_path1.split("/")[1:]
joined_path = os.path.join(*tmp_list)
#print("joined_path:", joined_path)
output_file_path = os.path.join(output_path, video_name, "target_gt", joined_path)
#print("output_file_path:", output_file_path)
dir_path = output_file_path.split("/")[:-1]
dir_path = "/".join(dir_path)
os.makedirs(
dir_path, #debug
exist_ok=True,)
cv2.imwrite(
output_file_path, #debug
out,
)
# querty_gt
for i,ann in enumerate(data["first_frame_anns"]):
mask = decode(ann["segmentation"])
mask = downsample(mask, query_img)
out = blend_mask(query_img, mask, color=color[0])
# os.makedirs(
# f"{output_path}/{video_name}/query_gt", #debug
# exist_ok=True,
# )
new_path_query = data['first_frame_image'].replace(f"{video_name}/", "")
output_file_path = os.path.join(output_path, video_name, "query_gt", new_path_query)
#print("new_path_query:", new_path_query)
#print("query_path:", f"{output_path}/{video_name}/query_gt/{new_path_query}")
dir_path = output_file_path.split("/")[:-1]
dir_path = "/".join(dir_path)
os.makedirs(
dir_path, #debug
exist_ok=True,)
cv2.imwrite(
output_file_path, #debug
out,
)
# prediction
mask_path = os.path.join(mask_base_path, data['image'])
#print(mask_path)
mask_path = mask_path.replace(".jpg", ".png")
mask = Image.open(mask_path)
mask = np.array(mask)
unique_instances = np.unique(mask)
unique_instances = unique_instances[unique_instances != 0]
if len(unique_instances) > 9:
continue
for i,instance_value in enumerate(unique_instances):
binary_mask = (mask == instance_value).astype(np.uint8)
binary_mask = cv2.resize(binary_mask, (target_img.shape[1], target_img.shape[0]))
try:
binary_mask = upsample_mask(binary_mask, target_img)
frame = blend_mask(target_img, binary_mask, color=color[i])
except:
breakpoint()
new_path_predict = data['image'].replace(f"{video_name}/", "")
#print("new_path_predict:", new_path_predict)
#print("predict_path:", f"{output_path}/{video_name}/predict/{new_path_predict}")
output_file_path = os.path.join(output_path, video_name, "predict", new_path_predict)
dir_path = output_file_path.split("/")[:-1]
dir_path = "/".join(dir_path)
os.makedirs(
dir_path, #debug
exist_ok=True,)
cv2.imwrite(
output_file_path, #debug
frame,
)
# os.makedirs(
# f"{output_path}/{video_name}/predict", #debug
# exist_ok=True,
# )
# cv2.imwrite(
# f"{output_path}/{video_name}/predict/{new_path_predict}", #debug
# frame,
# )
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