Upload 44 files

unigaze/configs/config.yaml

@@ -0,0 +1,38 @@
+defaults:
+  - _self_
+  - exp: exp_224
+  
+
+
+mode: train
+random_seed: 42
+num_workers: 20
+
+
+test_per_epoch: 1
+print_freq: 100
+data_sanity_check: false
+log_wandb: false
+output_dir: "./logs"
+ckpt_resume: null
+pretrain_ckptpath: null
+
+
+optimizer_cfg: null
+scheduler_cfg: null
+
+batch_size: 50
+test_batch_size: 200
+
+epochs: 25
+valid_epoch: 1
+eval_epoch: 10
+save_epoch: 10
+
+
+use_autocast: False
+
+batchnorm_type:
+  label: clean
+  unlabel: aug
+  test: clean

unigaze/configs/data/eyediap_cs.yaml

@@ -0,0 +1,22 @@
+type: datasets.eyediap.EYEDIAPDataset
+params:
+  data_name: eyediap_cs
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: 
+    - 'person_1.h5'
+    - 'person_2.h5'
+    - 'person_3.h5'
+    - 'person_4.h5'
+    - 'person_5.h5'
+    - 'person_6.h5'
+    - 'person_7.h5'
+    - 'person_8.h5'
+    - 'person_9.h5'
+    - 'person_10.h5'
+    - 'person_11.h5' 
+    - 'person_14.h5'
+    - 'person_15.h5'
+    - 'person_16.h5'

unigaze/configs/data/eyediap_cs_test.yaml

@@ -0,0 +1,22 @@
+type: datasets.eyediap.EYEDIAPDataset
+params:
+  data_name: eyediap_cs
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: 
+    # - 'person_1.h5'
+    # - 'person_2.h5'
+    # - 'person_3.h5'
+    # - 'person_4.h5'
+    # - 'person_5.h5'
+    # - 'person_6.h5'
+    # - 'person_7.h5'
+    # - 'person_8.h5'
+    - 'person_9.h5'
+    - 'person_10.h5'
+    - 'person_11.h5' 
+    - 'person_14.h5'
+    - 'person_15.h5'
+    - 'person_16.h5'

unigaze/configs/data/eyediap_cs_train.yaml

@@ -0,0 +1,22 @@
+type: datasets.eyediap.EYEDIAPDataset
+params:
+  data_name: eyediap_cs
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: 
+    - 'person_1.h5'
+    - 'person_2.h5'
+    - 'person_3.h5'
+    - 'person_4.h5'
+    - 'person_5.h5'
+    - 'person_6.h5'
+    - 'person_7.h5'
+    - 'person_8.h5'
+    # - 'person_9.h5'
+    # - 'person_10.h5'
+    # - 'person_11.h5' 
+    # - 'person_14.h5'
+    # - 'person_15.h5'
+    # - 'person_16.h5'

unigaze/configs/data/eyediap_ft.yaml

@@ -0,0 +1,24 @@
+type: datasets.eyediap.EYEDIAPDataset
+params:
+  data_name: eyediap_ft
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: 
+    - 'person_1.h5'
+    - 'person_2.h5'
+    - 'person_3.h5'
+    - 'person_4.h5'
+    - 'person_5.h5'
+    - 'person_6.h5'
+    - 'person_7.h5'
+    - 'person_8.h5'
+    - 'person_9.h5'
+    - 'person_10.h5'
+    - 'person_11.h5' 
+    - 'person_12.h5' 
+    - 'person_13.h5' 
+    - 'person_14.h5'
+    - 'person_15.h5'
+    - 'person_16.h5'

unigaze/configs/data/eyediap_ft_test.yaml

@@ -0,0 +1,24 @@
+type: datasets.eyediap.EYEDIAPDataset
+params:
+  data_name: eyediap_ft
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: 
+    # - 'person_1.h5'
+    # - 'person_2.h5'
+    # - 'person_3.h5'
+    # - 'person_4.h5'
+    # - 'person_5.h5'
+    # - 'person_6.h5'
+    # - 'person_7.h5'
+    # - 'person_8.h5'
+    - 'person_9.h5'
+    - 'person_10.h5'
+    - 'person_11.h5' 
+    - 'person_12.h5' 
+    - 'person_13.h5' 
+    - 'person_14.h5'
+    - 'person_15.h5'
+    - 'person_16.h5'

unigaze/configs/data/eyediap_ft_train.yaml

@@ -0,0 +1,24 @@
+type: datasets.eyediap.EYEDIAPDataset
+params:
+  data_name: eyediap_ft
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: 
+    - 'person_1.h5'
+    - 'person_2.h5'
+    - 'person_3.h5'
+    - 'person_4.h5'
+    - 'person_5.h5'
+    - 'person_6.h5'
+    - 'person_7.h5'
+    - 'person_8.h5'
+    # - 'person_9.h5'
+    # - 'person_10.h5'
+    # - 'person_11.h5' 
+    # - 'person_12.h5' 
+    # - 'person_13.h5' 
+    # - 'person_14.h5'
+    # - 'person_15.h5'
+    # - 'person_16.h5'

unigaze/configs/data/gaze360_test.yaml

@@ -0,0 +1,74 @@
+
+
+
+type: datasets.gaze360.Gaze360Dataset
+params:
+  data_name: gaze360_224_test
+  saved_norm_config:
+    focal_norm: 960
+    distance_norm: 600
+    roi_size: [224, 224]
+  norm_config:
+      focal_norm: 960
+      distance_norm: 600
+      roi_size: [224, 224]
+      
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  image_size: 224
+  sample_rate_use: 1
+  whether_crop_resize: False
+  keys_to_use:
+    - 000000.h5
+    - 000001.h5
+    - 000002.h5
+    - 000003.h5
+    - 000004.h5
+    - 000010.h5
+    - 000014.h5
+    - 000022.h5
+    - 000031.h5
+    - 000032.h5
+    - 000044.h5
+    - 000045.h5
+    - 000057.h5
+    - 000058.h5
+    - 000070.h5
+    - 000078.h5
+    - 000270.h5
+    - 000277.h5
+    - 000278.h5
+    - 000279.h5
+    - 000316.h5
+    - 000364.h5
+    - 000367.h5
+    - 000511.h5
+    - 000512.h5
+    - 000513.h5
+    - 000515.h5
+    - 000527.h5
+    - 000536.h5
+    - 000543.h5
+    - 000579.h5
+    - 000584.h5
+    - 000585.h5
+    - 000600.h5
+    - 000603.h5
+    - 000604.h5
+    - 000611.h5
+    - 000614.h5
+    - 000615.h5
+    - 000616.h5
+    - 000649.h5
+    - 000650.h5
+    - 000651.h5
+    - 000652.h5
+    - 000687.h5
+    - 000723.h5
+    - 000777.h5
+    - 000782.h5
+    - 000823.h5
+    - 000907.h5
+    - 000909.h5
+    - 000982.h5

unigaze/configs/data/gaze360_train.yaml

@@ -0,0 +1,252 @@
+
+
+
+type: datasets.gaze360.Gaze360Dataset
+params:
+  data_name: gaze360_224_train
+  saved_norm_config:
+    focal_norm: 960
+    distance_norm: 600
+    roi_size: [224, 224]
+  norm_config:
+      focal_norm: 960
+      distance_norm: 600
+      roi_size: [224, 224]
+      
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  image_size: 224
+  sample_rate_use: 1
+
+  keys_to_use:
+    - 000000.h5
+    - 000001.h5
+    - 000002.h5
+    - 000003.h5
+    - 000004.h5
+    - 000006.h5
+    - 000007.h5
+    - 000009.h5
+    - 000010.h5
+    - 000011.h5
+    - 000013.h5
+    - 000016.h5
+    - 000019.h5
+    - 000020.h5
+    - 000029.h5
+    - 000030.h5
+    - 000031.h5
+    - 000032.h5
+    - 000034.h5
+    - 000035.h5
+    - 000038.h5
+    - 000039.h5
+    - 000043.h5
+    - 000048.h5
+    - 000049.h5
+    - 000050.h5
+    - 000058.h5
+    - 000060.h5
+    - 000061.h5
+    - 000062.h5
+    - 000063.h5
+    - 000072.h5
+    - 000073.h5
+    - 000074.h5
+    - 000075.h5
+    - 000076.h5
+    - 000077.h5
+    - 000081.h5
+    - 000083.h5
+    - 000084.h5
+    - 000085.h5
+    - 000090.h5
+    - 000093.h5
+    - 000094.h5
+    - 000099.h5
+    - 000109.h5
+    - 000111.h5
+    - 000112.h5
+    - 000116.h5
+    - 000122.h5
+    - 000134.h5
+    - 000146.h5
+    - 000148.h5
+    - 000149.h5
+    - 000150.h5
+    - 000151.h5
+    - 000152.h5
+    - 000154.h5
+    - 000156.h5
+    - 000158.h5
+    - 000159.h5
+    - 000160.h5
+    - 000161.h5
+    - 000162.h5
+    - 000165.h5
+    - 000166.h5
+    - 000170.h5
+    - 000171.h5
+    - 000172.h5
+    - 000184.h5
+    - 000185.h5
+    - 000186.h5
+    - 000187.h5
+    - 000188.h5
+    - 000189.h5
+    - 000190.h5
+    - 000202.h5
+    - 000205.h5
+    - 000206.h5
+    - 000208.h5
+    - 000214.h5
+    - 000216.h5
+    - 000217.h5
+    - 000219.h5
+    - 000220.h5
+    - 000221.h5
+    - 000222.h5
+    - 000228.h5
+    - 000237.h5
+    - 000248.h5
+    - 000250.h5
+    - 000255.h5
+    - 000256.h5
+    - 000257.h5
+    - 000258.h5
+    - 000262.h5
+    - 000278.h5
+    - 000283.h5
+    - 000284.h5
+    - 000287.h5
+    - 000288.h5
+    - 000297.h5
+    - 000298.h5
+    - 000299.h5
+    - 000300.h5
+    - 000324.h5
+    - 000368.h5
+    - 000369.h5
+    - 000408.h5
+    - 000409.h5
+    - 000410.h5
+    - 000418.h5
+    - 000440.h5
+    - 000441.h5
+    - 000449.h5
+    - 000457.h5
+    - 000458.h5
+    - 000459.h5
+    - 000460.h5
+    - 000461.h5
+    - 000463.h5
+    - 000494.h5
+    - 000501.h5
+    - 000502.h5
+    - 000509.h5
+    - 000510.h5
+    - 000511.h5
+    - 000512.h5
+    - 000513.h5
+    - 000514.h5
+    - 000515.h5
+    - 000517.h5
+    - 000519.h5
+    - 000529.h5
+    - 000541.h5
+    - 000547.h5
+    - 000548.h5
+    - 000549.h5
+    - 000550.h5
+    - 000551.h5
+    - 000552.h5
+    - 000565.h5
+    - 000566.h5
+    - 000569.h5
+    - 000571.h5
+    - 000573.h5
+    - 000574.h5
+    - 000586.h5
+    - 000587.h5
+    - 000588.h5
+    - 000589.h5
+    - 000592.h5
+    - 000597.h5
+    - 000603.h5
+    - 000604.h5
+    - 000605.h5
+    - 000611.h5
+    - 000613.h5
+    - 000617.h5
+    - 000620.h5
+    - 000623.h5
+    - 000634.h5
+    - 000635.h5
+    - 000636.h5
+    - 000639.h5
+    - 000640.h5
+    - 000641.h5
+    - 000642.h5
+    - 000643.h5
+    - 000644.h5
+    - 000645.h5
+    - 000650.h5
+    - 000656.h5
+    - 000658.h5
+    - 000659.h5
+    - 000660.h5
+    - 000661.h5
+    - 000662.h5
+    - 000670.h5
+    - 000671.h5
+    - 000677.h5
+    - 000683.h5
+    - 000714.h5
+    - 000721.h5
+    - 000723.h5
+    - 000738.h5
+    - 000741.h5
+    - 000742.h5
+    - 000744.h5
+    - 000751.h5
+    - 000755.h5
+    - 000761.h5
+    - 000762.h5
+    - 000763.h5
+    - 000764.h5
+    - 000765.h5
+    - 000768.h5
+    - 000777.h5
+    - 000779.h5
+    - 000780.h5
+    - 000781.h5
+    - 000783.h5
+    - 000786.h5
+    - 000787.h5
+    - 000789.h5
+    - 000800.h5
+    - 000801.h5
+    - 000802.h5
+    - 000803.h5
+    - 000813.h5
+    - 000815.h5
+    - 000816.h5
+    - 000831.h5
+    - 000834.h5
+    - 000835.h5
+    - 000838.h5
+    - 000861.h5
+    - 000862.h5
+    - 000899.h5
+    - 000900.h5
+    - 000916.h5
+    - 000918.h5
+    - 000923.h5
+    - 000935.h5
+    - 000946.h5
+    - 000971.h5
+    - 000978.h5
+    - 000990.h5
+    - 000991.h5
+    - 001092.h5

unigaze/configs/data/gazecapture_test.yaml

@@ -0,0 +1,15 @@
+type: datasets.gazecapture.GazeCaptureDataset
+params:
+  data_name: gazecapture_test
+  color_type: rgb
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  keys_to_use: [ "00010.h5", "00110.h5", "00126.h5", "00178.h5", "00190.h5", "00192.h5", "00220.h5", "00222.h5", "00233.h5", "00319.h5", "00330.h5", "00343.h5", "00382.h5", "00460.h5", "00509.h5", "00511.h5", "00546.h5", "00563.h5", "00580.h5", "00585.h5", 
+    "00611.h5", "00616.h5", "00619.h5", "00646.h5", "00654.h5", "00680.h5", "00686.h5", "00700.h5", "00721.h5", "00741.h5", "00777.h5", "00796.h5", "00868.h5", "00921.h5", "00935.h5", "00949.h5", "00953.h5", 
+    "00965.h5", "00968.h5", "01036.h5", "01041.h5", "01051.h5", "01091.h5", "01148.h5", "01152.h5", "01155.h5", "01183.h5", "01200.h5", "01273.h5", "01278.h5", "01286.h5", "01326.h5", "01329.h5", "01370.h5", "01376.h5", "01425.h5", "01457.h5", "01477.h5", 
+    "01506.h5", "01517.h5", "01525.h5", "01575.h5", "01625.h5", "01672.h5", "01674.h5", "01689.h5", "01782.h5", "01794.h5", "01813.h5", "01830.h5", "01855.h5", "01863.h5", "01877.h5", "01893.h5", "01941.h5", "01959.h5", "01978.h5", 
+    "01983.h5", "01985.h5", "01997.h5", "02006.h5", "02020.h5", "02043.h5", "02078.h5", "02091.h5", "02109.h5", "02197.h5", "02213.h5", "02239.h5", "02240.h5", "02269.h5", "02275.h5", "02281.h5", "02292.h5", 
+    "02301.h5", "02348.h5", "02413.h5", "02419.h5", "02450.h5", "02455.h5", "02461.h5", "02480.h5", "02536.h5", "02601.h5", "02734.h5", "02755.h5", "02756.h5", "02805.h5", "02833.h5", "02851.h5", "02885.h5", "02899.h5", "02942.h5", "02966.h5", 
+    "02986.h5", "03011.h5", "03024.h5", "03043.h5", "03117.h5", "03126.h5", "03140.h5", "03177.h5", "03183.h5", "03185.h5", "03202.h5", "03216.h5", "03223.h5", "03247.h5", "03270.h5", "03324.h5", "03326.h5", "03344.h5", "03352.h5", "03361.h5", "03366.h5", 
+    "03404.h5", "03412.h5", "03451.h5", "03523.h5"]

unigaze/configs/data/gazecapture_test_ds15.yaml

@@ -0,0 +1,16 @@
+type: datasets.gazecapture.GazeCaptureDataset
+params:
+  data_name: gazecapture_test
+  color_type: rgb
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  sample_rate_use: 15
+  dataset_path: null
+  keys_to_use: [ "00010.h5", "00110.h5", "00126.h5", "00178.h5", "00190.h5", "00192.h5", "00220.h5", "00222.h5", "00233.h5", "00319.h5", "00330.h5", "00343.h5", "00382.h5", "00460.h5", "00509.h5", "00511.h5", "00546.h5", "00563.h5", "00580.h5", "00585.h5", 
+    "00611.h5", "00616.h5", "00619.h5", "00646.h5", "00654.h5", "00680.h5", "00686.h5", "00700.h5", "00721.h5", "00741.h5", "00777.h5", "00796.h5", "00868.h5", "00921.h5", "00935.h5", "00949.h5", "00953.h5", 
+    "00965.h5", "00968.h5", "01036.h5", "01041.h5", "01051.h5", "01091.h5", "01148.h5", "01152.h5", "01155.h5", "01183.h5", "01200.h5", "01273.h5", "01278.h5", "01286.h5", "01326.h5", "01329.h5", "01370.h5", "01376.h5", "01425.h5", "01457.h5", "01477.h5", 
+    "01506.h5", "01517.h5", "01525.h5", "01575.h5", "01625.h5", "01672.h5", "01674.h5", "01689.h5", "01782.h5", "01794.h5", "01813.h5", "01830.h5", "01855.h5", "01863.h5", "01877.h5", "01893.h5", "01941.h5", "01959.h5", "01978.h5", 
+    "01983.h5", "01985.h5", "01997.h5", "02006.h5", "02020.h5", "02043.h5", "02078.h5", "02091.h5", "02109.h5", "02197.h5", "02213.h5", "02239.h5", "02240.h5", "02269.h5", "02275.h5", "02281.h5", "02292.h5", 
+    "02301.h5", "02348.h5", "02413.h5", "02419.h5", "02450.h5", "02455.h5", "02461.h5", "02480.h5", "02536.h5", "02601.h5", "02734.h5", "02755.h5", "02756.h5", "02805.h5", "02833.h5", "02851.h5", "02885.h5", "02899.h5", "02942.h5", "02966.h5", 
+    "02986.h5", "03011.h5", "03024.h5", "03043.h5", "03117.h5", "03126.h5", "03140.h5", "03177.h5", "03183.h5", "03185.h5", "03202.h5", "03216.h5", "03223.h5", "03247.h5", "03270.h5", "03324.h5", "03326.h5", "03344.h5", "03352.h5", "03361.h5", "03366.h5", 
+    "03404.h5", "03412.h5", "03451.h5", "03523.h5"]

unigaze/configs/data/gazecapture_train.yaml

@@ -0,0 +1,1189 @@
+type: datasets.gazecapture.GazeCaptureDataset
+params:
+  data_name: gazecapture_train_224
+  color_type: rgb
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  sample_rate_use: 1
+  keys_to_use:
+    - 00002.h5
+    - 00003.h5
+    - 00005.h5
+    - 00006.h5
+    - 00024.h5
+    - 00028.h5
+    - 00033.h5
+    - 00034.h5
+    - 00087.h5
+    - 00089.h5
+    - 00097.h5
+    - 00098.h5
+    - 00099.h5
+    - 00102.h5
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unigaze/configs/data/gazecapture_train_ds15.yaml

@@ -0,0 +1,1189 @@
+type: datasets.gazecapture.GazeCaptureDataset
+params:
+  data_name: gazecapture_train_224
+  color_type: rgb
+  transform_type: 'basic_imagenet'
+  image_size: 224
+  dataset_path: null
+  sample_rate_use: 15
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+    - 03248.h5
+    - 03251.h5
+    - 03253.h5
+    - 03255.h5
+    - 03259.h5
+    - 03263.h5
+    - 03265.h5
+    - 03266.h5
+    - 03273.h5
+    - 03275.h5
+    - 03277.h5
+    - 03278.h5
+    - 03282.h5
+    - 03283.h5
+    - 03302.h5
+    - 03303.h5
+    - 03304.h5
+    - 03307.h5
+    - 03314.h5
+    - 03315.h5
+    - 03327.h5
+    - 03328.h5
+    - 03332.h5
+    - 03336.h5
+    - 03340.h5
+    - 03342.h5
+    - 03343.h5
+    - 03348.h5
+    - 03351.h5
+    - 03354.h5
+    - 03358.h5
+    - 03359.h5
+    - 03360.h5
+    - 03367.h5
+    - 03371.h5
+    - 03374.h5
+    - 03375.h5
+    - 03377.h5
+    - 03378.h5
+    - 03379.h5
+    - 03381.h5
+    - 03382.h5
+    - 03384.h5
+    - 03397.h5
+    - 03403.h5
+    - 03406.h5
+    - 03413.h5
+    - 03425.h5
+    - 03431.h5
+    - 03432.h5
+    - 03435.h5
+    - 03442.h5
+    - 03453.h5
+    - 03454.h5
+    - 03456.h5
+    - 03463.h5
+    - 03465.h5
+    - 03466.h5
+    - 03467.h5
+    - 03469.h5
+    - 03473.h5
+    - 03491.h5
+    - 03492.h5
+    - 03495.h5
+    - 03498.h5
+    - 03501.h5
+    - 03502.h5
+

unigaze/configs/data/mpiigaze.yaml

@@ -0,0 +1,24 @@
+type: datasets.mpiigaze.MPIIGazeDataset
+params:
+  data_name: mpii
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  image_size: 224
+  keys_to_use: 
+    - p00.h5
+    - p01.h5
+    - p02.h5
+    - p03.h5
+    - p04.h5
+    - p05.h5
+    - p06.h5
+    - p07.h5
+    - p08.h5
+    - p09.h5
+    - p10.h5
+    - p11.h5
+    - p12.h5
+    - p13.h5
+    - p14.h5
+

unigaze/configs/data/mpiigaze_test.yaml

@@ -0,0 +1,24 @@
+type: datasets.mpiigaze.MPIIGazeDataset
+params:
+  data_name: mpii
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  image_size: 224
+  keys_to_use: 
+    # - p00.h5
+    # - p01.h5
+    # - p02.h5
+    # - p03.h5
+    # - p04.h5
+    # - p05.h5
+    # - p06.h5
+    # - p07.h5
+    # - p08.h5
+    # - p09.h5
+    - p10.h5
+    - p11.h5
+    - p12.h5
+    - p13.h5
+    - p14.h5
+

unigaze/configs/data/mpiigaze_train.yaml

@@ -0,0 +1,24 @@
+type: datasets.mpiigaze.MPIIGazeDataset
+params:
+  data_name: mpii
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  image_size: 224
+  keys_to_use: 
+    - p00.h5
+    - p01.h5
+    - p02.h5
+    - p03.h5
+    - p04.h5
+    - p05.h5
+    - p06.h5
+    - p07.h5
+    - p08.h5
+    - p09.h5
+    # - p10.h5
+    # - p11.h5
+    # - p12.h5
+    # - p13.h5
+    # - p14.h5
+

unigaze/configs/data/xgaze_0_60sub.yaml

@@ -0,0 +1,76 @@
+
+type: datasets.xgaze.XGazeDataset
+params:
+  data_name: xgaze_v2_224
+
+  images_per_frame: 18
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  frame_tag: 'all'
+  image_size: 224
+  keys_to_use: 
+    - subject0000.h5
+    - subject0003.h5
+    - subject0004.h5
+    - subject0005.h5
+    - subject0006.h5
+    - subject0007.h5
+    - subject0008.h5
+    - subject0009.h5
+    - subject0010.h5
+    - subject0013.h5
+    - subject0014.h5
+    - subject0015.h5
+    - subject0016.h5
+    - subject0018.h5
+    - subject0019.h5
+    - subject0021.h5
+    - subject0024.h5
+    - subject0026.h5
+    - subject0027.h5
+    - subject0028.h5
+    - subject0029.h5
+    - subject0030.h5
+    - subject0031.h5
+    - subject0032.h5
+    - subject0033.h5
+    - subject0035.h5
+    - subject0036.h5
+    - subject0038.h5
+    - subject0039.h5
+    - subject0040.h5
+    - subject0041.h5
+    - subject0043.h5
+    - subject0044.h5
+    - subject0045.h5
+    - subject0046.h5
+    - subject0048.h5
+    - subject0050.h5
+    - subject0051.h5
+    - subject0052.h5
+    - subject0055.h5
+    - subject0056.h5
+    - subject0057.h5
+    - subject0058.h5
+    - subject0059.h5
+    - subject0060.h5
+    - subject0061.h5
+    - subject0062.h5
+    - subject0063.h5
+    - subject0065.h5
+    - subject0066.h5
+    - subject0067.h5
+    - subject0069.h5
+    - subject0072.h5
+    - subject0073.h5
+    - subject0075.h5
+    - subject0076.h5
+    - subject0078.h5
+    - subject0079.h5
+    - subject0080.h5
+    - subject0081.h5
+    
+
+
+    

unigaze/configs/data/xgaze_0_60sub_d3.yaml

@@ -0,0 +1,80 @@
+
+type: datasets.xgaze.XGazeDataset
+params:
+  data_name: xgaze_v2_224
+    
+  images_per_frame: 18
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  frame_tag: 'all'
+  camera_random: 3
+  image_size: 224
+
+
+
+  keys_to_use: 
+    - subject0000.h5
+    - subject0003.h5
+    - subject0004.h5
+    - subject0005.h5
+    - subject0006.h5
+    - subject0007.h5
+    - subject0008.h5
+    - subject0009.h5
+    - subject0010.h5
+    - subject0013.h5
+    - subject0014.h5
+    - subject0015.h5
+    - subject0016.h5
+    - subject0018.h5
+    - subject0019.h5
+    - subject0021.h5
+    - subject0024.h5
+    - subject0026.h5
+    - subject0027.h5
+    - subject0028.h5
+    - subject0029.h5
+    - subject0030.h5
+    - subject0031.h5
+    - subject0032.h5
+    - subject0033.h5
+    - subject0035.h5
+    - subject0036.h5
+    - subject0038.h5
+    - subject0039.h5
+    - subject0040.h5
+    - subject0041.h5
+    - subject0043.h5
+    - subject0044.h5
+    - subject0045.h5
+    - subject0046.h5
+    - subject0048.h5
+    - subject0050.h5
+    - subject0051.h5
+    - subject0052.h5
+    - subject0055.h5
+    - subject0056.h5
+    - subject0057.h5
+    - subject0058.h5
+    - subject0059.h5
+    - subject0060.h5
+    - subject0061.h5
+    - subject0062.h5
+    - subject0063.h5
+    - subject0065.h5
+    - subject0066.h5
+    - subject0067.h5
+    - subject0069.h5
+    - subject0072.h5
+    - subject0073.h5
+    - subject0075.h5
+    - subject0076.h5
+    - subject0078.h5
+    - subject0079.h5
+    - subject0080.h5
+    - subject0081.h5
+    
+
+
+    

unigaze/configs/data/xgaze_0_80sub.yaml

@@ -0,0 +1,97 @@
+
+type: datasets.xgaze.XGazeDataset
+params:
+  data_name: xgaze_v2_224
+    
+  images_per_frame: 18
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  frame_tag: 'all'
+  image_size: 224
+
+  keys_to_use: 
+    - subject0000.h5
+    - subject0003.h5
+    - subject0004.h5
+    - subject0005.h5
+    - subject0006.h5
+    - subject0007.h5
+    - subject0008.h5
+    - subject0009.h5
+    - subject0010.h5
+    - subject0013.h5
+    - subject0014.h5
+    - subject0015.h5
+    - subject0016.h5
+    - subject0018.h5
+    - subject0019.h5
+    - subject0021.h5
+    - subject0024.h5
+    - subject0026.h5
+    - subject0027.h5
+    - subject0028.h5
+    - subject0029.h5
+    - subject0030.h5
+    - subject0031.h5
+    - subject0032.h5
+    - subject0033.h5
+    - subject0035.h5
+    - subject0036.h5
+    - subject0038.h5
+    - subject0039.h5
+    - subject0040.h5
+    - subject0041.h5
+    - subject0043.h5
+    - subject0044.h5
+    - subject0045.h5
+    - subject0046.h5
+    - subject0048.h5
+    - subject0050.h5
+    - subject0051.h5
+    - subject0052.h5
+    - subject0055.h5
+    - subject0056.h5
+    - subject0057.h5
+    - subject0058.h5
+    - subject0059.h5
+    - subject0060.h5
+    - subject0061.h5
+    - subject0062.h5
+    - subject0063.h5
+    - subject0065.h5
+    - subject0066.h5
+    - subject0067.h5
+    - subject0069.h5
+    - subject0072.h5
+    - subject0073.h5
+    - subject0075.h5
+    - subject0076.h5
+    - subject0078.h5
+    - subject0079.h5
+    - subject0080.h5
+    - subject0081.h5
+    - subject0083.h5
+    - subject0084.h5
+    - subject0085.h5
+    - subject0088.h5
+    - subject0090.h5
+    - subject0092.h5
+    - subject0095.h5
+    - subject0098.h5
+    - subject0099.h5
+    - subject0100.h5
+    - subject0101.h5
+    - subject0102.h5
+    - subject0103.h5
+    - subject0104.h5
+    - subject0105.h5
+    - subject0106.h5
+    - subject0107.h5
+    - subject0108.h5
+    - subject0109.h5
+    - subject0111.h5
+    
+
+
+    

unigaze/configs/data/xgaze_0_80sub_d3.yaml

@@ -0,0 +1,97 @@
+
+type: datasets.xgaze.XGazeDataset
+params:
+  data_name: xgaze_v2_224
+  color_type: bgr
+  images_per_frame: 18
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  frame_tag: 'all'
+  camera_random: 3
+  image_size: 224
+
+  keys_to_use: 
+    - subject0000.h5
+    - subject0003.h5
+    - subject0004.h5
+    - subject0005.h5
+    - subject0006.h5
+    - subject0007.h5
+    - subject0008.h5
+    - subject0009.h5
+    - subject0010.h5
+    - subject0013.h5
+    - subject0014.h5
+    - subject0015.h5
+    - subject0016.h5
+    - subject0018.h5
+    - subject0019.h5
+    - subject0021.h5
+    - subject0024.h5
+    - subject0026.h5
+    - subject0027.h5
+    - subject0028.h5
+    - subject0029.h5
+    - subject0030.h5
+    - subject0031.h5
+    - subject0032.h5
+    - subject0033.h5
+    - subject0035.h5
+    - subject0036.h5
+    - subject0038.h5
+    - subject0039.h5
+    - subject0040.h5
+    - subject0041.h5
+    - subject0043.h5
+    - subject0044.h5
+    - subject0045.h5
+    - subject0046.h5
+    - subject0048.h5
+    - subject0050.h5
+    - subject0051.h5
+    - subject0052.h5
+    - subject0055.h5
+    - subject0056.h5
+    - subject0057.h5
+    - subject0058.h5
+    - subject0059.h5
+    - subject0060.h5
+    - subject0061.h5
+    - subject0062.h5
+    - subject0063.h5
+    - subject0065.h5
+    - subject0066.h5
+    - subject0067.h5
+    - subject0069.h5
+    - subject0072.h5
+    - subject0073.h5
+    - subject0075.h5
+    - subject0076.h5
+    - subject0078.h5
+    - subject0079.h5
+    - subject0080.h5
+    - subject0081.h5
+    - subject0083.h5
+    - subject0084.h5
+    - subject0085.h5
+    - subject0088.h5
+    - subject0090.h5
+    - subject0092.h5
+    - subject0095.h5
+    - subject0098.h5
+    - subject0099.h5
+    - subject0100.h5
+    - subject0101.h5
+    - subject0102.h5
+    - subject0103.h5
+    - subject0104.h5
+    - subject0105.h5
+    - subject0106.h5
+    - subject0107.h5
+    - subject0108.h5
+    - subject0109.h5
+    - subject0111.h5
+    
+
+
+    

unigaze/configs/data/xgaze_60_80sub.yaml

@@ -0,0 +1,31 @@
+type: datasets.xgaze.XGazeDataset
+params:
+  data_name: xgaze_v2_224
+  images_per_frame: 18
+  color_type: bgr
+  transform_type: 'basic_imagenet'
+  dataset_path: null
+  frame_tag: 'all'
+  image_size: 224
+  keys_to_use: 
+    - subject0083.h5
+    - subject0084.h5
+    - subject0085.h5
+    - subject0088.h5
+    - subject0090.h5
+    - subject0092.h5
+    - subject0095.h5
+    - subject0098.h5
+    - subject0099.h5
+    - subject0100.h5
+    - subject0101.h5
+    - subject0102.h5
+    - subject0103.h5
+    - subject0104.h5
+    - subject0105.h5
+    - subject0106.h5
+    - subject0107.h5
+    - subject0108.h5
+    - subject0109.h5
+    - subject0111.h5
+  

unigaze/configs/exp/blank.yaml

@@ -0,0 +1,22 @@
+
+
+
+
+exp_name: tbd
+
+
+exp_explanation: 
+
+data: null 
+
+## can be overwritten
+model: null 
+
+
+trainer:  null
+loss:  null
+
+
+optimizer: configs/optimizers/default_Adam_e4.yaml
+scheduler: configs/schedulers/default_stepLR_5.yaml
+

unigaze/configs/exp/cross/train_ED.yaml

@@ -0,0 +1,27 @@
+
+
+
+train: 
+  - configs/data/eyediap_cs.yaml
+  - configs/data/eyediap_ft.yaml
+
+
+
+val: 
+  
+  - configs/data/mpiigaze_train.yaml
+  - configs/data/mpiigaze_test.yaml
+  
+
+test: 
+  # - configs/data/xgaze_0_80sub.yaml
+  - configs/data/xgaze_0_60sub.yaml
+  - configs/data/xgaze_60_80sub.yaml
+  
+  - configs/data/gazecapture_train.yaml
+  - configs/data/gazecapture_test.yaml
+  - configs/data/gaze360_train.yaml
+  - configs/data/gaze360_test.yaml
+
+  - configs/data/mpiigaze.yaml
+  - configs/data/our_mpii.yaml

unigaze/configs/exp/cross/train_G360.yaml

@@ -0,0 +1,32 @@
+
+train: 
+  - configs/data/gaze360_train.yaml
+  - configs/data/gaze360_test.yaml
+
+
+val: 
+  - configs/data/mpiigaze_train.yaml
+  - configs/data/mpiigaze_test.yaml
+
+  - configs/data/eyediap_cs_train.yaml
+  - configs/data/eyediap_cs_test.yaml
+  - configs/data/eyediap_ft_train.yaml
+  - configs/data/eyediap_ft_test.yaml
+
+  
+
+
+test: 
+  # - configs/data/xgaze_0_80sub.yaml
+  - configs/data/xgaze_0_60sub.yaml
+  - configs/data/xgaze_60_80sub.yaml
+  - configs/data/gazecapture_train.yaml
+  - configs/data/gazecapture_test.yaml
+
+  - configs/data/mpiigaze.yaml
+  # - configs/data/our_mpii.yaml
+  
+  - configs/data/eyediap_cs.yaml
+  - configs/data/eyediap_ft.yaml
+
+  

unigaze/configs/exp/cross/train_GC.yaml

@@ -0,0 +1,26 @@
+
+train: 
+  - configs/data/gazecapture_train_ds15.yaml
+  - configs/data/gazecapture_test_ds15.yaml
+
+
+
+val: 
+  - configs/data/mpiigaze_train.yaml
+  - configs/data/mpiigaze_test.yaml
+
+  - configs/data/eyediap_cs_train.yaml
+  - configs/data/eyediap_cs_test.yaml
+  - configs/data/eyediap_ft_train.yaml
+  - configs/data/eyediap_ft_test.yaml
+
+
+test: 
+
+  - configs/data/xgaze_0_60sub.yaml
+  - configs/data/xgaze_60_80sub.yaml
+  - configs/data/gaze360_train.yaml
+  - configs/data/gaze360_test.yaml
+  - configs/data/mpiigaze.yaml
+  - configs/data/eyediap_cs.yaml
+  - configs/data/eyediap_ft.yaml

unigaze/configs/exp/cross/train_MPII.yaml

@@ -0,0 +1,24 @@
+
+
+train: 
+  - configs/data/mpiigaze.yaml
+
+val: 
+  
+  - configs/data/eyediap_cs_train.yaml
+  - configs/data/eyediap_cs_test.yaml
+  - configs/data/eyediap_ft_train.yaml
+  - configs/data/eyediap_ft_test.yaml
+  
+
+test: 
+  - configs/data/xgaze_0_60sub.yaml
+  - configs/data/xgaze_60_80sub.yaml
+  - configs/data/gazecapture_train.yaml
+  - configs/data/gazecapture_test.yaml
+  - configs/data/gaze360_train.yaml
+  - configs/data/gaze360_test.yaml
+
+
+  - configs/data/eyediap_cs.yaml
+  - configs/data/eyediap_ft.yaml

unigaze/configs/exp/cross/train_X.yaml

@@ -0,0 +1,27 @@
+
+
+
+train: 
+  - configs/data/xgaze_0_80sub_d3.yaml
+
+
+val: 
+ 
+  - configs/data/mpiigaze_train.yaml
+  - configs/data/mpiigaze_test.yaml
+  
+  - configs/data/eyediap_cs_train.yaml
+  - configs/data/eyediap_cs_test.yaml
+  - configs/data/eyediap_ft_train.yaml
+  - configs/data/eyediap_ft_test.yaml
+
+test: 
+  - configs/data/gazecapture_train.yaml
+  - configs/data/gazecapture_test.yaml
+  - configs/data/gaze360_train.yaml
+  - configs/data/gaze360_test.yaml
+
+  - configs/data/mpiigaze.yaml
+
+  - configs/data/eyediap_cs.yaml
+  - configs/data/eyediap_ft.yaml

unigaze/configs/exp/joint/X_GC_M_ED_g360.yaml

@@ -0,0 +1,23 @@
+
+
+train: 
+  - configs/data/xgaze_0_60sub_d3.yaml
+  - configs/data/gazecapture_train_ds15.yaml
+  - configs/data/mpiigaze_train.yaml
+  - configs/data/eyediap_cs_train.yaml
+  - configs/data/eyediap_ft_train.yaml
+  - configs/data/gaze360_train.yaml
+
+
+val: 
+  - configs/data/eyediap_cs_test.yaml
+  - configs/data/eyediap_ft_test.yaml
+  - configs/data/gaze360_test.yaml
+  - configs/data/mpiigaze_test.yaml
+  - configs/data/our_mpii_test.yaml
+
+test: 
+  - configs/data/xgaze_60_80sub.yaml
+  - configs/data/gazecapture_test.yaml
+
+

unigaze/configs/loss/l1_loss.yaml

@@ -0,0 +1,4 @@
+loss_config:
+  type: criteria.gaze_loss.PitchYawLoss
+  params:
+    loss_type: 'l1'

unigaze/configs/model/hybrid_tr50.yaml

@@ -0,0 +1,5 @@
+
+net_config:
+  type: models.hybrid_tr.HybridTR50
+  
+

unigaze/configs/model/mae_b_16_gaze.yaml

@@ -0,0 +1,11 @@
+
+
+
+
+net_config:
+  type: models.vit.mae_gaze.MAE_Gaze
+  params: 
+    model_type: 'vit_b_16'
+    global_pool: False
+    drop_path_rate: 0.1
+    custom_pretrained_path: checkpoints/mae_b16/mae_b16_checkpoint-299.pth

unigaze/configs/model/mae_h_14_gaze.yaml

@@ -0,0 +1,14 @@
+# ViT-Huge patchsize=14
+# data_yamls="${ffhqnv_yaml} ${facesyn_yaml} ${sfhq_t2i_224_yaml} ${vfhq_yaml} ${celebv_yaml} ${vgg2_yaml} ${xgaze_mvs_dense_224_yaml}"
+
+
+net_config:
+  type: models.vit.mae_gaze.MAE_Gaze
+  params: 
+    model_type: 'vit_h_14'
+    global_pool: False
+    drop_path_rate: 0.1
+    custom_pretrained_path: checkpoints/mae_h14/mae_h14_checkpoint-299.pth
+
+
+

unigaze/configs/model/mae_l_16_gaze.yaml

@@ -0,0 +1,11 @@
+
+s
+net_config:
+  type: models.vit.mae_gaze.MAE_Gaze
+  params: 
+    model_type: 'vit_l_16'
+    global_pool: False
+    drop_path_rate: 0.1
+    custom_pretrained_path: checkpoints/mae_l16/mae_h14_checkpoint-299.pth
+
+

unigaze/configs/model/res18.yaml

@@ -0,0 +1,5 @@
+
+net_config:
+  type: models.resnet.Res18
+  
+

unigaze/configs/model/res50.yaml

@@ -0,0 +1,6 @@
+
+
+net_config:
+  type: models.resnet.Res50
+  
+

unigaze/configs/model/vit_b_16_gaze.yaml

@@ -0,0 +1,11 @@
+
+
+net_config:
+  type: models.vit.vit_gaze.ViTGaze
+  params: 
+    vit_type: 'b_16'
+
+
+
+
+

unigaze/configs/model/vit_h_14_gaze.yaml

@@ -0,0 +1,19 @@
+
+
+# net_config:
+#   type: models.vit.vit_gaze.CustomViT_H14
+#   params: 
+#     imagenet_pretrained_path: checkpoints/vit_pretrain/vit_h_14_swag-80465313.pth
+    
+    
+
+
+net_config:
+  type: models.vit.vit_gaze.CustomViT_H14
+  params: 
+    global_pool: False
+    drop_path_rate: 0.1
+    custom_pretrained_path: checkpoints/vit_pretrain/timm_vit_h_14_imagenet21k.bin
+
+
+

unigaze/configs/model/vit_l_16_gaze.yaml

@@ -0,0 +1,11 @@
+
+
+net_config:
+  type: models.vit.vit_gaze.ViTGaze
+  params: 
+    vit_type: 'l_16'
+
+
+
+
+

unigaze/configs/optimizers/default_Adam_e4.yaml

@@ -0,0 +1,7 @@
+
+
+optimizer_name: Adam       # Optimizer type (e.g., Adam, SGD, AdamW)
+
+lr: 0.0001          # Initial learning rate
+
+weight_decay: 0.000001   # Weight decay (L2 regularization)

unigaze/configs/schedulers/OneCycleLR.yaml

@@ -0,0 +1,7 @@
+
+
+scheduler_name: OneCycleLR
+
+
+div_factor: 25.0
+final_div_factor: 10000.0

unigaze/configs/schedulers/default_stepLR_5.yaml

@@ -0,0 +1,7 @@
+
+
+scheduler_name: StepLR
+
+step_size: 5
+
+gamma: 0.1

unigaze/configs/trainer/simple_trainer.yaml

@@ -0,0 +1,12 @@
+
+
+type: trainers.simple_trainer.SimpleTrainer
+
+
+
+
+
+
+
+
+

unigaze/infer_runtime.py

@@ -0,0 +1,297 @@
+# unigaze/infer_runtime.py
+import os
+import cv2
+import time
+import tempfile
+from pathlib import Path
+from typing import Optional, Tuple, Dict, Any, List
+
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+
+# --- UniGaze / your repo imports (these exist in your repo) ---
+from datasets.helper.image_transform import wrap_transforms
+from utils import instantiate_from_cfg
+from utils.util import set_seed
+from gazelib.gaze.gaze_utils import pitchyaw_to_vector, vector_to_pitchyaw
+from gazelib.gaze.normalize import estimateHeadPose, normalize
+from gazelib.label_transform import get_face_center_by_nose
+import face_alignment
+
+# ---------------- Helpers copied from predict_gaze_video.py ----------------
+
+def draw_gaze(image_in, pitchyaw, thickness=8, color=(0, 0, 255)):
+    image_out = image_in.copy()
+    (h, w) = image_in.shape[:2]
+    length = w / 2.0
+    pos = (int(h / 2.0), int(w / 2.0))
+    if len(image_out.shape) == 2 or image_out.shape[2] == 1:
+        image_out = cv2.cvtColor(image_out, cv2.COLOR_GRAY2BGR)
+    dx = -length * np.sin(pitchyaw[1]) * np.cos(pitchyaw[0])
+    dy = -length * np.sin(pitchyaw[0])
+    end_point = (int(pos[0] + dx), int(pos[1] + dy))
+
+    shadow_offset = 2
+    shadow_color = (40, 40, 40)
+    shadow_end = (end_point[0] + shadow_offset, end_point[1] + shadow_offset)
+    cv2.arrowedLine(image_out, (pos[0] + shadow_offset, pos[1] + shadow_offset),
+                    shadow_end, shadow_color, thickness + 2, cv2.LINE_AA, tipLength=0.3)
+
+    thickness_values = [4, 3, 2, 1]
+    num_layers = len(thickness_values)
+    for i in range(num_layers):
+        alpha = i / num_layers
+        layer_color = tuple(int((1 - alpha) * color[j] + alpha * 255) for j in range(3))
+        cv2.arrowedLine(image_out, pos, end_point, layer_color, thickness_values[i],
+                        cv2.LINE_AA, tipLength=0.3)
+    return image_out
+
+def set_dummy_camera_model(image=None):
+    h, w = image.shape[:2]
+    focal_length = w * 4
+    center = (w // 2, h // 2)
+    camera_matrix = np.array(
+        [[focal_length, 0, center[0]],
+         [0, focal_length, center[1]],
+         [0, 0, 1]], dtype="double"
+    )
+    camera_distortion = np.zeros((1, 5))
+    return np.array(camera_matrix), np.array(camera_distortion)
+
+def denormalize_predicted_gaze(gaze_yaw_pitch, R_inv):
+    pred_gaze_cancel_nor = pitchyaw_to_vector(gaze_yaw_pitch.reshape(1, 2)).reshape(3, 1)
+    pred_gaze_cancel_nor = np.matmul(R_inv, pred_gaze_cancel_nor.reshape(3, 1))
+    pred_gaze_cancel_nor = pred_gaze_cancel_nor / np.linalg.norm(pred_gaze_cancel_nor)
+    pred_yaw_pitch_cancel_nor = vector_to_pitchyaw(pred_gaze_cancel_nor.reshape(1, 3))
+    return pred_gaze_cancel_nor, pred_yaw_pitch_cancel_nor
+
+def load_checkpoint(model, ckpt_key, ckpt_path):
+    assert os.path.isfile(ckpt_path), f"Checkpoint not found: {ckpt_path}"
+    weights = torch.load(ckpt_path, map_location="cpu")
+    model_state = weights[ckpt_key]
+    if next(iter(model_state.keys())).startswith("module."):
+        model_state = {k[7:]: v for k, v in model_state.items()}
+    model.load_state_dict(model_state, strict=True)
+    del weights
+
+# ---------------- Runtime (model + pre/post) ----------------
+
+class UniGazeRuntime:
+    def __init__(self, cfg_path: str, ckpt_path: str, device: str = "cpu"):
+        self.device = torch.device(device)
+        torch.set_grad_enabled(False)
+        set_seed(42)
+
+        pretrained_model_cfg = OmegaConf.load(cfg_path)["net_config"]
+        pretrained_model_cfg.params.custom_pretrained_path = None
+
+        self.model = instantiate_from_cfg(pretrained_model_cfg)
+        load_checkpoint(self.model, "model_state", ckpt_path)
+        self.model.eval().to(self.device)
+
+        self.transform = wrap_transforms("basic_imagenet", image_size=224)
+        self.fa = face_alignment.FaceAlignment(
+            face_alignment.LandmarksType.TWO_D,
+            flip_input=False,
+            device=self.device.type,  # 'cpu' or 'cuda'
+        )
+
+        # Constants from your script
+        self.focal_norm = 960
+        self.distance_norm = 600
+        self.roi_size = (224, 224)
+        self.resize_factor = 0.5
+        self.arrow_colors = [(47, 255, 173)]  # BGR
+
+    # ---- One-frame inference on a BGR frame; returns annotated BGR frame ----
+    def process_frame(self, image_original_bgr: np.ndarray) -> np.ndarray:
+        image_original = image_original_bgr.copy()
+    
+        # resize for detection
+        if self.resize_factor >= 1:
+            image_resize = image_original.copy()
+        else:
+            image_resize = cv2.resize(
+                image_original, dsize=None,
+                fx=self.resize_factor, fy=self.resize_factor,
+                interpolation=cv2.INTER_AREA
+            )
+    
+        image_resize = cv2.cvtColor(image_resize, cv2.COLOR_BGR2RGB)
+        preds = self.fa.get_landmarks(image_resize)
+    
+        # no face: just return original
+        if preds is None:
+            return image_original
+    
+        # ----- keep same semantics as predict_gaze_video.py -----
+        landmarks_record = {}                 # only add when a valid vector is produced
+        vector_start_end_point_list = {}      # start/end 2D points for arrows
+        bbox_record = {}                      # for drawing rectangles (same idx keys)
+    
+        for idx, landmarks_in_original in enumerate(preds):
+            color = self.arrow_colors[idx % len(self.arrow_colors)]
+    
+            # scale landmarks back to original size
+            landmarks_in_original = landmarks_in_original / self.resize_factor
+            x_min = int(landmarks_in_original[:, 0].min())
+            x_max = int(landmarks_in_original[:, 0].max())
+            y_min = int(landmarks_in_original[:, 1].min())
+            y_max = int(landmarks_in_original[:, 1].max())
+    
+            # bbox for drawing (scale 1.2)
+            scale_factor_draw = 1.2
+            bbox_width = x_max - x_min
+            bbox_height = y_max - y_min
+            bbox_center = ((x_min + x_max) // 2, (y_min + y_max) // 2)
+            x_min_draw = max(0, bbox_center[0] - int(bbox_width * scale_factor_draw // 2))
+            x_max_draw = min(image_original.shape[1], bbox_center[0] + int(bbox_width * scale_factor_draw // 2))
+            y_min_draw = max(0, bbox_center[1] - int(bbox_height * scale_factor_draw // 2))
+            y_max_draw = min(image_original.shape[0], bbox_center[1] + int(bbox_height * scale_factor_draw // 2))
+            bbox_record[idx] = (x_min_draw, y_min_draw, x_max_draw, y_max_draw)
+    
+            # crop for normalization & inference (scale 2.0)
+            scale_factor_crop = 2.0
+            bbox_width = x_max - x_min
+            bbox_height = y_max - y_min
+            bbox_center = ((x_min + x_max) // 2, (y_min + y_max) // 2)
+            x_min_c = max(0, bbox_center[0] - int(bbox_width * scale_factor_crop // 2))
+            x_max_c = min(image_original.shape[1], bbox_center[0] + int(bbox_width * scale_factor_crop // 2))
+            y_min_c = max(0, bbox_center[1] - int(bbox_height * scale_factor_crop // 2))
+            y_max_c = min(image_original.shape[0], bbox_center[1] + int(bbox_height * scale_factor_crop // 2))
+    
+            image = image_original[y_min_c:y_max_c, x_min_c:x_max_c]
+            landmarks = landmarks_in_original - np.array([x_min_c, y_min_c])
+    
+            # camera + head pose
+            camera_matrix, camera_distortion = set_dummy_camera_model(image=image)
+            face_model_load = np.loadtxt("data/face_model.txt")
+            face_model = face_model_load[[20, 23, 26, 29, 15, 19], :]
+            facePts = face_model.reshape(6, 1, 3)
+    
+            landmarks_sub = landmarks[[36, 39, 42, 45, 31, 35], :].astype(float).reshape(6, 1, 2)
+            hr, ht = estimateHeadPose(landmarks_sub, facePts, camera_matrix, camera_distortion)
+            hR = cv2.Rodrigues(hr)[0]
+            face_center_camera_cord, _ = get_face_center_by_nose(hR=hR, ht=ht, face_model_load=face_model_load)
+    
+            # normalize
+            img_normalized, R, hR_norm, _, _, _ = normalize(
+                image, landmarks, self.focal_norm, self.distance_norm,
+                self.roi_size, face_center_camera_cord, hr, ht, camera_matrix, gc=None
+            )
+    
+            # skip bad heads (same as script)
+            hr_norm = np.array([np.arcsin(hR_norm[1, 2]), np.arctan2(hR_norm[0, 2], hR_norm[2, 2])])
+            if np.linalg.norm(hr_norm) > 80 * np.pi / 180:
+                continue
+    
+            # inference
+            input_var = img_normalized[:, :, [2, 1, 0]]  # BGR->RGB
+            input_var = self.transform(input_var)
+            input_var = torch.as_tensor(input_var, dtype=torch.float32, device=self.device).unsqueeze(0)
+            with torch.no_grad():
+                ret = self.model(input_var)
+    
+            pred_gaze = ret["pred_gaze"][0]
+            pred_gaze_np = pred_gaze.detach().cpu().numpy()
+    
+            # denormalize to original camera coords, then project to 2D
+            R_inv = np.linalg.inv(R)
+            pred_gaze_cancel_nor, _ = denormalize_predicted_gaze(pred_gaze_np, R_inv)
+    
+            vec_length = pred_gaze_cancel_nor * -112 * 1.5
+            gazeRay = np.concatenate(
+                (face_center_camera_cord.reshape(1, 3),
+                 (face_center_camera_cord + vec_length).reshape(1, 3)),
+                axis=0,
+            )
+            result = cv2.projectPoints(
+                gazeRay,
+                np.array([0, 0, 0]).reshape(3, 1).astype(float),
+                np.array([0, 0, 0]).reshape(3, 1).astype(float),
+                camera_matrix, camera_distortion
+            )[0].reshape(2, 2)
+            result += np.array([x_min_c, y_min_c])
+    
+            vector_start_point = (int(result[0][0]), int(result[0][1]))
+            vector_end_point   = (int(result[1][0]), int(result[1][1]))
+    
+            # only record these after a valid vector exists
+            vector_start_end_point_list[idx] = (vector_start_point, vector_end_point)
+            landmarks_record[idx] = landmarks_in_original
+    
+        # If nothing valid was produced, return original frame
+        if not landmarks_record:
+            return image_original
+    
+        # ----- draw exactly like predict_gaze_video.py (iterate over landmarks_record) -----
+        for idx in list(landmarks_record.keys()):
+            x_min_d, y_min_d, x_max_d, y_max_d = bbox_record[idx]
+            color = self.arrow_colors[idx % len(self.arrow_colors)]
+    
+            cv2.rectangle(image_original, (x_min_d, y_min_d), (x_max_d, y_max_d), (0, 0, 240), 2)
+    
+            vsp, vep = vector_start_end_point_list[idx]
+            shadow_offset = 2
+            shadow_color = (40, 40, 40)
+            shadow_end = (vep[0] + shadow_offset, vep[1] + shadow_offset)
+            cv2.arrowedLine(
+                image_original,
+                (vsp[0] + shadow_offset, vsp[1] + shadow_offset),
+                shadow_end,
+                shadow_color,
+                5,
+                cv2.LINE_AA,
+                tipLength=0.2,
+            )
+    
+            thickness_values = [x * 3 for x in [4, 3, 2, 1]]
+            num_layers = len(thickness_values)
+            for i in range(num_layers):
+                alpha = i / num_layers
+                layer_color = tuple(int((1 - alpha) * color[j] + alpha * 255) for j in range(3))
+                cv2.arrowedLine(image_original, vsp, vep, layer_color, thickness_values[i], cv2.LINE_AA, tipLength=0.2)
+    
+        return image_original
+
+
+    # ---- Public APIs ----
+
+    def predict_image(self, image_rgb: np.ndarray) -> np.ndarray:
+        """Accepts an RGB image (HxWx3) and returns an annotated RGB image."""
+        bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
+        out_bgr = self.process_frame(bgr)
+        out_rgb = cv2.cvtColor(out_bgr, cv2.COLOR_BGR2RGB)
+        return out_rgb
+
+    def predict_video(self, video_path: str) -> Tuple[Optional[str], Optional[np.ndarray], float]:
+        """
+        Process a video file and return:
+          - temp MP4 path (string) for Gradio Video
+          - last annotated RGB frame (numpy) for Gradio Image
+          - total runtime seconds (float)
+        """
+        t0 = time.time()
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            return None, None, 0.0
+
+        width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps    = max(1, int(cap.get(cv2.CAP_PROP_FPS)) or 25)
+
+        tmp_mp4 = Path(tempfile.mkdtemp(prefix="unigaze_vid_")) / "out.mp4"
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        writer = cv2.VideoWriter(str(tmp_mp4), fourcc, fps, (width, height))
+
+        while True:
+            ret, frame_bgr = cap.read()
+            if not ret:
+                break
+            out_bgr = self.process_frame(frame_bgr)
+            writer.write(out_bgr)
+
+        cap.release()
+        writer.release()
+        return str(tmp_mp4), float(time.time() - t0)