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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
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
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']
data_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_dataset_whisks/train"
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"
takes_ids = os.listdir(data_path)
for take_id in tqdm.tqdm(takes_ids):
#实验需改动
prediction_path = os.path.join(mask_path, take_id)
if not os.path.exists(prediction_path):
print(take_id)
continue
prediction_path = os.path.join(prediction_path, "rgb")
file_names = natsorted(os.listdir(prediction_path))
idxs = [f.split(".")[0] for f in file_names]
#为了节省内存 实际上可以idx[:60]来可视化部分帧
for id in idxs:
frame_idx = id
frame = cv2.imread(
f"{data_path}/{take_id}/rgb/{frame_idx}.jpg"
)
mask = Image.open(f"{prediction_path}/{frame_idx}.png")
mask = np.array(mask)
unique_instances = np.unique(mask)
unique_instances = unique_instances[unique_instances != 0]
for i,instance_value in enumerate(unique_instances):
binary_mask = (mask == instance_value).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=color[0])
os.makedirs(
f"{output_path}/{take_id}/obj_{i}", #debug
exist_ok=True,
)
cv2.imwrite(
f"{output_path}/{take_id}/obj_{i}/{frame_idx}.jpg",
out,
)
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