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batch_blackgen_iou.py
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batch_blackgen_iou.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 = v_list[::-1]
# v_list = [v_list[1]]
# v_list = ["dashcam/dashcam_2"]
# v_list = [v_list[2]]
# v_list = ["visdrone/videos/vis_171"]
high = 30
tile = 16
# model_name = f"COCO_full_normalizedsaliency_R_101_FPN_crossthresh"
"""
For object detection, use bound 0.5, conv 9 for drone videos and dashcam videos.
Use
COCO_full_normalizedsaliency_R_101_FPN_crossthresh
as the model, and use
["dashcam/dashcam_%d" % i for i in range(1, 8)]
and
["visdrone/videos/vis_%d" % i for i in range(169, 174)]
for video id
"""
# conv_list = [3]
# bound_list = [0.05]
#
# for visdrone
# conv_list = [11]
# bound_list = [0.1]
# uniform color background
# conv_list = [1, 5, 9]
# bound_list = [0.15, 0.2, 0.25]
# base_list = [40, 36]
# conv_list = [1, 5]
# bound_list = [0.15, 0.1]
# base_list = [36]
# conv_list = [1]
# bound_list = [0.02]
# base_list = [51]
# conv_list = [1]
# bound_list = [0.1, 0.2]
# base_list = [40]
conv_list = [5]
bound_list = [0.2]
base_list = [50]
# conv_list = [1]
# bound_list = [0.2]
# base_list = [40]
# conv_list = [1, 5]
# bound_list = [0.1, 0.15, 0.05]
# base_list = [-1]
# model_name = f"cityscape_detection_FPN_SSD_withconfidence_allclasses_new_unfreezebackbone"
# v_list = ["videos/drone_%d" % i for i in range(7)] + [
# "videos/dashcamcropped_%d" % i for i in range(1, 11)
# ]
# v_list = ["videos/drone_%d" % i for i in range(7)]
# v_list = [v_list[i] for i in range(len(v_list)) if i % 2 == 0]
# v_list = ["videos/driving_%d" % i for i in range(4, 5)]
v_list = ["videos/dashcamcropped_%d" % i for i in range(1, 8)] + []
# "videos/driving_%d" % i for i in range(5)
# ]
# v_list = ["videos/surf_%d_final" % i for i in [1, 2, 3, 4, 6, 7]]
# v_list = ["videos/dashcamcropped_%d" % i for i in range(1, 2)]
# FPN
stats = "frozen_stats_MLSys/stats_QP30_thresh7_segmented_FPN"
conf_thresh = 0.7
gt_conf_thresh = 0.7
app_name = "COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"
# efficientdet
# stats = "frozen_stats_MLSys/stats_QP30_thresh4_segment_EfficientDet"
# conf_thresh = 0.4
# gt_conf_thresh = 0.4
# app_name = "EfficientDet"
# YoLo
# stats = "frozen_stats_MLSys/stats_QP30_thresh3_segment_Yolo"
# conf_thresh = 0.3
# gt_conf_thresh = 0.3
# app_name = "Yolo5s"
# segmentation
# stats = "frozen_stats_MLSys/stats_QP30_segment_fcn"
# app_name = "Segmentation/fcn_resnet50"
# conf_thresh = 0.7
# gt_conf_thresh = 0.7
model_app = "FPN"
model_name = f"COCO_detection_{model_app}_SSD_withconfidence_allclasses_new_unfreezebackbone_withoutclasscheck"
# model_name = "pretrainedkeypointmodel"
# model_app = "fcn"
visualize_step_size = 10000
# accs = [filter([fmt % i, "newSSDwconf", "bound_0.2", "lq_40", "conv_1"]) for i in ids]
import glob
# app_name = "Segmentation/fcn_resnet50"
# app_name = "EfficientDet"
filename = "SSD/accmpegmodel"
for conv, bound, base, v in product(conv_list, bound_list, base_list, v_list):
print(v, conv, bound, base)
# output = f'{v}_compressed_ground_truth_2%_tile_16.mp4'
# visdrone/videos/vis_169_blackgen_bound_0.2_qp_30_conv_5_app_FPN.mp4
# output = f"{v}_blackgen_bound_{bound}_qp_30_conv_{conv}_app_FPN.mp4"
output = f"{v}_roi_bound_{bound}_conv_{conv}_hq_{high}_lq_{base}_app_{model_app}.mp4"
# examine_output = (
# f"{v}_blackgen_dual_SSD_bound_{bound}_conv_{conv}_app_FPN.mp4"
# )
# os.system(f"rm -r {examine_output}*")
if True:
os.system(
f"python compress_blackgen_roi_iou.py -i {v}_qp_{high}.mp4 "
f" {v}_qp_{high}.mp4 -s {v} -o {output} --tile_size {tile} -p maskgen_pths/{model_name}.pth.best"
f" --conv_size {conv} "
f" -g {v}_qp_{high}.mp4 --bound {bound} --hq {high} --lq {base} --smooth_frames 10 --app {app_name} "
f"--maskgen_file /tank/kuntai/code/video-compression/maskgen/{filename}.py --visualize_step_size {visualize_step_size}"
)
# os.system(
# f"python inference.py -i {output} --app {app_name} --confidence_threshold {conf_thresh} --gt_confidence_threshold {gt_conf_thresh} --visualize_step_size {visualize_step_size} "
# # f" --visualize --lq_result {v}_qp_{base}.mp4 --ground_truth {v}_qp_{high}.mp4"
# )
# os.system(
# f"python examine.py -i {output} -g {v}_qp_{high}.mp4 --confidence_threshold {conf_thresh} --gt_confidence_threshold {gt_conf_thresh} --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",
# ]
# )