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batch_object_dds.py
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batch_object_dds.py
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import os
from itertools import product
from pdb import set_trace
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 = ["visdrone/videos/vis_%d" % i for i in range(169, 174)] + [
# "dashcam/dashcam_%d" % i for i in range(1, 11)
# ]
# v_list = ["dashcam/dashcam_5"]
# v_list = ["visdrone/videos/vis_%d" % i for i in [170, 171]]
v_list = ["visdrone/videos/vis_%d" % i for i in [169, 170, 171, 172, 173]]
# v_list = ["visdrone/videos/vis_%d" % i for i in [171]]
# v_list = [v_list[2]]
high = 30
tile = 16
perc = 5
base_high_list = [(50, 30)]
# app_name = "COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"
app_name = "Segmentation/fcn_resnet50"
conv_list = [1]
dual = True
for v, (base, high), conv in product(v_list, base_high_list, conv_list):
# output = f'{v}_compressed_ground_truth_2%_tile_16.mp4'
orig = f"{v}_compressed_blackgen_ddsseg3_conv_{conv}_qp_{high}.mp4"
# output = f"{v}_compressed_blackgen_dual_obj_qp_{qp}_conv_{conv}.mp4"
if not os.path.exists(orig):
# os.system(f"python inference.py -i {v}_qp_30.mp4 --app {app_name}")
os.system(
f"python compress_object_segmentation.py -i {v}_qp_30.mp4 -g {v}_qp_{base}.mp4 "
f"-s {v} -o {orig} --qp {high} --app {app_name} --conv_size {conv}"
)
if dual:
output = f"{v}_compressed_blackgen_dual_ddsseg3_conv_{conv}_qp_{base}_{high}.mp4"
os.system(f"rm -r {output}*")
os.system(f"cp {orig} {output}.qp30.mp4")
# set_trace()
os.system(f"cp {orig}.mask {output}.qp30.mp4.mask")
os.system(f"cp {orig}.args {output}.qp30.mp4.args")
os.system(f"cp {v}_qp_{base}.mp4 {output}.base.mp4")
orig = output
os.system(f"python inference.py -i {orig} --app {app_name}")
os.system(
f"python examine.py -i {orig} -g {v}_qp_{high}.mp4 --gt_confidence_threshold 0.7 --confidence_threshold 0.7 --stats stats_fcn50_measurement_new --app {app_name}"
)