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batch_autoencoder.py
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batch_autoencoder.py
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import os
import subprocess
from itertools import product
import yaml
# v_list = ['dashcam_%d_test' % (i+1) for i in range(4)] + ['trafficcam_%d_test' % (i+1) for i in range(4)]
# v_list = [v_list[0]]
# v_list = ["youtube_videos/dashcam_%d_crop" % (i + 1) for i in range(4)] + [
# "youtube_videos/trafficcam_%d_crop" % (i + 1) for i in range(4)
# ]
# v_list = ["dashcam/dashcam_%d" % i for i in [2, 5, 6, 8]]
# v_list = ["visdrone/videos/vis_%d" % i for i in range(169, 174)] + [
# "dashcam/dashcam_%d" % i for i in range(1, 11)
# ]
# v_list = ["adapt/drive_%d" % i for i in range(30, 60)]
# v_list = ["dashcam/dashcam_%d" % i for i in [7]]
v_list = [
# "large_object/large_%d" % i
# for i in range(3, 5)
# "visdrone/videos/vis_169",
'visdrone/videos/vis_%d' % i for i in [170, 172, 173]
# "visdrone/videos/vis_171",
# "visdrone/videos/vis_170",
# "visdrone/videos/vis_173",
# "visdrone/videos/vis_169",
# "visdrone/videos/vis_172",
# "visdrone/videos/vis_209",
# "visdrone/videos/vis_217",
] # + ["dashcam/dashcam_%d" % i for i in range(1, 11)]
# v_list = [v_list[2]]
# v_list = ["visdrone/videos/vis_171"]
base = 50
high = 30
tile = 16
model_name = f"COCO_full_normalizedsaliency_R_101_FPN_crossthresh"
conv_list = [9]
bound_list = [0.5]
stats = "stats_FPN"
# app_name = "Segmentation/fcn_resnet50"
app_name = "COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"
for v in v_list:
os.system(
f"python inference.py -i {v}_autoencoder.mp4 --app {app_name} --confidence_threshold 0.7"
)
os.system(
f"python examine.py -i {v}_autoencoder.mp4 -g {v}_qp_{high}.mp4 --confidence_threshold 0.7 --gt_confidence_threshold 0.7 --app {app_name} --stats {stats}"
)
# if not os.path.exists(f"diff/{output}.gtdiff.mp4"):
# gt_output = f"{v}_compressed_blackgen_gt_bbox_conv_{conv}.mp4"
# subprocess.run(
# [
# "python",
# "diff.py",
# "-i",
# output,
# gt_output,
# "-o",
# f"diff/{output}.gtdiff.mp4",
# ]
# )