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import json
import cv2
import numpy as np
from pathlib import Path
import os
from PIL import Image
if __name__ == "__main__":
'''get frame_id'''
# with open("/scratch/yuqian_fu/egoexo_val_framelevel_newprompt_all_instruction.json") as f:
# data = json.load(f)
# data_new = []
# for item in data:
# if item['video_name'] == "1247a29c-9fda-47ac-8b9c-78b1e76e977e":
# data_new.append(item)
# test_sample = data_new[0]
# print(test_sample['new_img_id'])
'''vis_mask'''
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 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
mask_path = "/scratch/yuqian_fu/test_result/mask/1247a29c-9fda-47ac-8b9c-78b1e76e977e_ref/30_pred_complex_ego_watch.png"
img_path = "/scratch/yuqian_fu/test_data/1247a29c-9fda-47ac-8b9c-78b1e76e977e/aria01_214-1/30.jpg"
mask = Image.open(mask_path)
mask = np.array(mask)
print(mask.shape)
mask2 = cv2.imread(mask_path)
print(type(mask2), mask2.shape)
frame = cv2.imread(img_path)
unique_instances = np.unique(mask)
unique_instances = unique_instances[unique_instances != 0]
if len(unique_instances) != 0:
for i,instance in enumerate(unique_instances):
binary_mask = (mask == instance).astype(np.uint8)
binary_mask = cv2.resize(binary_mask, (frame.shape[1], frame.shape[0]))
binary_mask = upsample_mask(binary_mask, frame)
out = blend_mask(frame, binary_mask, color="g")
save_path = "/scratch/yuqian_fu/test_result/img/1247a29c-9fda-47ac-8b9c-78b1e76e977e_ref/30_pred_complex_ego_watch.jpg"
Path(os.path.dirname(save_path)).mkdir(parents=True, exist_ok=True)
cv2.imwrite(save_path, out)
'''change insttruction'''
# with open("/scratch/yuqian_fu/egoexo_val_framelevel_newprompt_all_instruction.json") as f:
# data = json.load(f)
# data_new = []
# for item in data:
# if item['video_name'] == "1247a29c-9fda-47ac-8b9c-78b1e76e977e":
# data_new.append(item)
# test_sample = data_new[0]
# # print(test_sample['new_img_id'])
# # print(test_sample['image'])
# # print(test_sample['instruction'])
# instruction_list = []
# sample = {
# "tokens": ['the', 'ball'],
# "raw": "the ball.",
# "sent_id": 2203,
# "sent": "the ball"
# }
# image_info = {
# 'file_name': test_sample['first_frame_image'],
# 'height': 704,
# 'width': 704,
# }
# instruction_list.append(sample)
# to_save = {
# "image":test_sample['first_frame_image'],
# "image_info":image_info,
# "anns":test_sample['first_frame_anns'],
# "first_frame_image":test_sample['first_frame_image'],
# "first_frame_anns":test_sample['first_frame_anns'],
# "new_img_id":test_sample['new_img_id'],
# "video_name":test_sample['video_name'],
# "instruction":instruction_list
# }
# save_path = "/scratch/yuqian_fu/sample_instruction_ego.json"
# with open(save_path, "w") as f:
# json.dump([to_save], f)
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