def full():
"""
global variable
"""
global PANDA_TEST_SIZE,fliterscore,UP_boundary,iskeep_dets,test,isdel_inter,isfliter
PANDA_TEST_SIZE=[
[26573,15052], # 9
[32609,24457], # 10
[31746,23810],
[34682,26012],
[26583,14957],
[26583,14957],
[26573,15052]]
UP_boundary=[3552,2004,0,0,5990,4257,3813]
#是否use keep_dets()
iskeep_dets=0
isfliter=1#fliter score xml
isdel_inter=0#
test=1#"test"
fliterscore={"14_OCT":0,"15_nanshan":0,"1601_shool":0,"1602_shool":0,"17_newzhongguan":0,"1801_xilin":0,"1802_xilin":0}
nms_name="emnms"
nms_thresh=0.5
network="detectors"
weights=""
cls_="elema"#head
dataset="selfbicyle"
resfile="/root/data/gvision/mmdetection-master/workdir/detectors_vehicle_method2_elema/output/test_bbox.json"
# resfile="/root/data/gvision/mmdetection-master/workdir/detectors_1_2_fafaxue/output/detectors_ep30_12_fafaxue_bbox.json"
splitannofile='/root/data/rubzz/ruby/ruby_output/test/vehicle/method3_bbox_small_else_noresize.json'
imgfilters=["15_27","14_02","16_01","17_23","18_01","18_40","18_41"]
outfile=f"{network}{weights}_delinter{isdel_inter}isfliter{isfliter}{cls_}dataset{dataset}{nms_name}{nms_thresh}.json"#save image prefix
image_prefix=f'delinter{isdel_inter}isfliter{isfliter}{nms_name}{nms_thresh}'
# savepath=f"/root/data/gvision/my_merge/{cls_}/visual/{network}_{dataset}"
#softnms nms setnms emnms
if test:
outpath=f"/root/data/gvision/my_merge/{cls_}/coco_results/{test}/{network}_{dataset}"
else:
outpath=f"/root/data/gvision/my_merge/{cls_}/coco_results/{network}_{dataset}"
savepath=os.path.join(outpath,"visual")
merge=DetResMerge(resfile=resfile,outpath=outpath,
# splitannofile="/root/data/rubzz/ruby/ruby_output/test/person/split_test_method2_person.json",
splitannofile=splitannofile,
srcannofile="/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
# srcannofile="/root/data/gvision/dataset/predict/17_01/image_annos/1701.json",
npyname=outfile[:-4]+f"{nms_name}",
test=test,
imgfilters=imgfilters,
isload_npy=False,
)
merge.mergeResults(outfile=outfile,is_nms=True,nms_thresh=nms_thresh,nms_name=nms_name)
# merge.mergeResults(merge_input_mode="xywh",is_nms=True,nms_thresh=0.9)
merge_result_visual(image_folder_test="/root/data/gvision/dataset/raw_data/image_test",
result_path=os.path.join(outpath,outfile),
annos_path="/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
savepath=savepath,
output_prefix=image_prefix,
imgfilters=imgfilters,
test=test,
mode="xywh",
num=10,score=0.3)#mode: input_bbox mode
def head():
"""
global variable
"""
global PANDA_TEST_SIZE,fliterscore,UP_boundary,iskeep_dets,test,isdel_inter,isfliter
PANDA_TEST_SIZE=[
[26573,15052], # 9
[32609,24457], # 10
[31760,23810],
[26583,14957],
[26583,14957],
[26573,15052]]
UP_boundary=[4500,1417,0,0,5357,4205,52]
#是否use keep_dets()
iskeep_dets=0
isfliter=1#fliter score xml
isdel_inter=0#
test=1
fliterscore={"14_OCT":0.1,"15_nanshan":0.1,"1601_shool":0.1,"1602_shool":0.1,"17_newzhongguan":0.1,"1801_xilin":0.1,"1802_xilin":0.1}
nms_name="softnms"
nms_thresh=0.7
network="retinaface"
weights="final_model"
cls_="head"#head
dataset="person_unsure"
resfile="/root/data/gvision/detectron2-master/workdir/output/my_head_retinaface_ms_panda/my_predict/final_nms0.7_fs0.1_all.json"
splitannofile="/root/data/rubzz/ruby/ruby_output2/test_person_unsure_cell.json"
imgfilters=["15_27","14_02","16_01","17_23","18_01"]
outfile=f"{network}{weights}_delinter{isdel_inter}isfliter{isfliter}{cls_}dataset{dataset}{nms_name}{nms_thresh}.json"#save image prefix
image_prefix=f'delinter{isdel_inter}isfliter{isfliter}{nms_name}{nms_thresh}'
# savepath=f"/root/data/gvision/my_merge/{cls_}/visual/{network}_{dataset}"
#softnms nms setnms emnms
outpath=f"/root/data/gvision/my_merge/{cls_}/coco_results/{network}_{dataset}"
savepath=os.path.join(outpath,"visual")
merge=DetResMerge(resfile=resfile,outpath=outpath,
# splitannofile="/root/data/rubzz/ruby/ruby_output/test/person/split_test_method2_person.json",
splitannofile=splitannofile,
srcannofile="/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
# srcannofile="/root/data/gvision/dataset/predict/17_01/image_annos/1701.json",
npyname=outfile[:-4]+f"{nms_name}",
test=test,
imgfilters=imgfilters,
isload_npy=False,
)
merge.mergeResults(outfile=outfile,is_nms=True,nms_thresh=nms_thresh,nms_name=nms_name)
# merge.mergeResults(merge_input_mode="xywh",is_nms=True,nms_thresh=0.9)
merge_result_visual(image_folder_test="/root/data/gvision/dataset/raw_data/image_test",
result_path=os.path.join(outpath,outfile),
annos_path="/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
savepath=savepath,
output_prefix=image_prefix,
imgfilters=imgfilters,
test=test,
mode="xywh",
num=10)#mode: input_bbox mode
def full():
"""
global variable
"""
global PANDA_TEST_SIZE, fliterscore, UP_boundary, keep_fliter, test
PANDA_TEST_SIZE = [
[26573, 15052], # 9
[32609, 24457], # 10
[31760, 23810],
[26583, 14957],
[26583, 14957],
[26573, 15052]
]
UP_boundary = [4500, 1417, 0, 0, 5357, 4205, 52]
#是否use keep_dets()
keep_fliter = 1
fliterscore = {
"14_OCT": 0.1,
"15_nanshan": 0.5,
"1601_shool": 0.2,
"1602_shool": 0.3,
"17_newzhongguan": 0.4,
"1801_xilin": 0.4,
"1802_xilin": 0.4
}
network = "crowdet"
weights = ""
cls_ = "full" #head
test = 1
resfile = "/root/data/gvision/CrowdDet-master/model/rcnn_emd_refinet/visiblebody/outputs/eval_dump/allvboxdump_3nms0.35prethre0.4.json"
# resfile="/root/data/gvision/CrowdDet-master/model/rcnn_emd_refinet/outputs/eval_dump/testalldunms0.35prethre0.4.json"
outpath = f"/root/data/gvision/my_merge/{cls_}/coco_results"
imgfilters = [
"14_02", "16_02", "16_14", "16_16", "15_24", "17_02", "18_40", "18_01",
"17_01"
]
imgfilters = ["15_24"]
outfile = f"inter{network}{weights}_{cls_}.json" #save image prefix
savepath = f"/root/data/gvision/my_merge/{cls_}/visual/{network}"
#softnms nms setnms emnms
nms_name = "softnms"
nms_thresh = 0.7
merge = DetResMerge(
resfile=resfile,
outpath=outpath,
# splitannofile="/root/data/rubzz/ruby/ruby_output/test/person/split_test_method2_person.json",
splitannofile=
"/root/data/gvision/dataset/predict/person/test_person.json",
srcannofile=
"/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
# srcannofile="/root/data/gvision/dataset/predict/17_01/image_annos/1701.json",
npyname=outfile[:-4] + f"{nms_name}",
test=test,
imgfilters=imgfilters,
isload_npy=False,
)
merge.mergeResults(outfile=outfile,
is_nms=True,
nms_thresh=nms_thresh,
nms_name=nms_name)
# merge.mergeResults(merge_input_mode="xywh",is_nms=True,nms_thresh=0.9)
merge_result_visual(
image_folder_test="/root/data/gvision/dataset/raw_data/image_test",
result_path=os.path.join(outpath, outfile),
annos_path=
"/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
savepath=savepath,
output_prefix=outfile[:-4] + f"{nms_name}{nms_thresh}",
imgfilters=imgfilters,
test=test,
mode="xywh",
num=10) #mode: input_bbox mode
def vehicle():
"""
global variable
"""
global PANDA_TEST_SIZE,fliterscore,UP_boundary,iskeep_dets,test,isdel_inter,isfliter
PANDA_TEST_SIZE=[
[26573,15052], # 9
[32609,24457], # 10
[31760,23810],
[26583,14957],
[26583,14957],
[26573,15052]]
UP_boundary=[4500,1417,0,0,5357,4205,52]
#是否use keep_dets()
iskeep_dets=0
isfliter=1#fliter score xml
isdel_inter=1#
test=0
fliterscore={"14_OCT":0.0,"15_nanshan":0,"1601_shool":0.2,"1602_shool":0.3,"17_newzhongguan":0,"1801_xilin":0.1,"1802_xilin":0}
nms_name="nms"
nms_thresh=0.2
network="detectors"
weights=""
cls_="vehicle"#head
dataset="else"
resfile="/root/data/gvision/mmdetection-master/workdir/detectors_vehicle_method2/output/coco_bicycle_and_panda_else/split_reuslt_detectros_else_bbox.json"
# resfile="/root/data/gvision/mmdetection-master/workdir/detectors_vehicle_method2/output/coco_bicycle_and_panda_else/split_reuslt_detectros_else_big_bbox.json"
# resfile="/root/data/gvision/mmdetection-master/workdir/detectors_vehicle_method2/output/epoch_12_car/split_reuslt_detectros_car_big_bbox.json"
# resfile="/root/data/gvision/mmdetection-master/workdir/detectors_vehicle_method2/output/epoch_12_car/split_reuslt_detectros_car_17_bbox.json"
# resfile="/root/data/gvision/mmdetection-master/workdir/detectors_vehicle_method2/output/epoch_12_car/split_reuslt_detectros_car_without17_bbox.json"
splitannofile='/root/data/rubzz/ruby/ruby_output/test/vehicle/method3_bbox_small_else_noresize.json'
# splitannofile='/root/data/rubzz/ruby/ruby_output/test/person/split_test_method2_bigimageto1536.json'
# splitannofile="/root/data/rubzz/ruby/ruby_output/test/vehicle/test_bbox_vehicle_1333_1238_17_noresize.json"
# splitannofile="/root/data/rubzz/ruby/ruby_output/test/vehicle/test_bbox_vehicle_1333_1238_without17.json"
# imgfilters=["14_02","16_02" ,"16_14","16_16","15_24","17_02" ,"18_40","18_01","17_01"]
# imgfilters=["15_24","14_02","17_01","17_011","17_015","17_26","17_27"]
imgfilters=["15_27","14_02","16_01","17_23","18_01"]
outfile=f"{network}{weights}_delinter{isdel_inter}isfliter{isfliter}{cls_}dataset{dataset}{nms_name}{nms_thresh}.json"#save image prefix
outfile="car_without_17.json"
image_prefix=f'delinter{isdel_inter}isfliter{isfliter}{nms_name}{nms_thresh}'
savepath=f"/root/data/gvision/my_merge/{cls_}/visual/{network}_{dataset}"
#softnms nms setnms emnms
outpath=f"/root/data/gvision/my_merge/{cls_}/coco_results/detectors"
merge=DetResMerge(resfile=resfile,outpath=outpath,
# splitannofile="/root/data/rubzz/ruby/ruby_output/test/person/split_test_method2_person.json",
splitannofile=splitannofile,
srcannofile="/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
# srcannofile="/root/data/gvision/dataset/predict/17_01/image_annos/1701.json",
npyname=outfile[:-4]+f"{nms_name}",
test=test,
imgfilters=imgfilters,
isload_npy=False,
)
merge.mergeResults(outfile=outfile,is_nms=True,nms_thresh=nms_thresh,nms_name=nms_name)
# merge.mergeResults(merge_input_mode="xywh",is_nms=True,nms_thresh=0.9)
merge_result_visual(image_folder_test="/root/data/gvision/dataset/raw_data/image_test",
result_path=os.path.join(outpath,outfile),
annos_path="/root/data/gvision/dataset/raw_data/image_annos/person_bbox_test.json",
savepath=savepath,
output_prefix=image_prefix,
imgfilters=imgfilters,
test=test,
mode="xywh",
num=10)#mode: input_bbox mode
