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Binary Dataset Creator for Individual Action Mastery
Creates binary classifiers: [Target Action] vs [All Other Actions]
This is the foundation for the Evolution Strategy:
1. Master individual actions (95%+ accuracy each)
2. Combine into categories
3. Build confidence-based frontend
"""
import torch
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import os
import warnings
warnings.filterwarnings("ignore")
# UCF101 class names (in alphabetical order)
UCF101_CLASSES = [
'ApplyEyeMakeup', 'ApplyLipstick', 'Archery', 'BabyCrawling', 'BalanceBeam',
'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk', 'BenchPress',
'Biking', 'Billiards', 'BlowDryHair', 'BlowingCandles', 'BodyWeightSquats',
'Bowling', 'BoxingPunchingBag', 'BoxingSpeedBag', 'BreastStroke', 'BrushingTeeth',
'CleanAndJerk', 'CliffDiving', 'CricketBowling', 'CricketShot', 'CuttingInKitchen',
'Diving', 'Drumming', 'Fencing', 'FieldHockeyPenalty', 'FloorGymnastics',
'FrisbeeCatch', 'FrontCrawl', 'GolfSwing', 'Haircut', 'Hammering',
'HammerThrow', 'HandstandPushups', 'HandstandWalking', 'HeadMassage', 'HighJump',
'HorseRace', 'HorseRiding', 'HulaHoop', 'IceDancing', 'JavelinThrow',
'JugglingBalls', 'JumpingJack', 'JumpRope', 'Kayaking', 'Knitting',
'LongJump', 'Lunges', 'MilitaryParade', 'Mixing', 'MoppingFloor',
'Nunchucks', 'ParallelBars', 'PizzaTossing', 'PlayingCello', 'PlayingDaf',
'PlayingDhol', 'PlayingFlute', 'PlayingGuitar', 'PlayingPiano', 'PlayingSitar',
'PlayingTabla', 'PlayingViolin', 'PoleVault', 'PommelHorse', 'PullUps',
'Punch', 'PushUps', 'Rafting', 'RockClimbingIndoor', 'RopeClimbing',
'Rowing', 'SalsaSpin', 'ShavingBeard', 'Shotput', 'SkateBoarding',
'Skiing', 'Skijet', 'SkyDiving', 'SoccerJuggling', 'SoccerPenalty',
'StillRings', 'SumoWrestling', 'Surfing', 'Swing', 'TableTennisShot',
'TaiChi', 'TennisSwing', 'ThrowDiscus', 'TrampolineJumping', 'Typing',
'UnevenBars', 'VolleyballSpiking', 'WalkingWithDog', 'WallPushups', 'WritingOnBoard',
'YoYo'
]
class VideoTransform:
"""Optimized video transform for binary classification"""
def __init__(self):
self.resize = transforms.Resize((112, 112))
self.normalize = transforms.Normalize(
mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989]
)
def __call__(self, clip):
if clip is None:
return torch.randn(3, 16, 112, 112)
if clip.dim() != 4:
return torch.randn(3, 16, 112, 112)
# Convert to proper format [C, T, H, W]
if clip.shape[3] == 3: # THWC format
clip = clip.permute(3, 0, 1, 2)
clip = clip.float() / 255.0
# Apply transforms to each frame
transformed_frames = []
for t in range(clip.size(1)):
frame = clip[:, t, :, :]
frame = self.resize(frame)
frame = self.normalize(frame)
transformed_frames.append(frame)
return torch.stack(transformed_frames, dim=1)
class BinaryActionDataset(Dataset):
"""
Binary Classification Dataset: [Target Action] vs [All Others]
This is the KEY to the Evolution Strategy:
- Train ONLY on basketball vs non-basketball
- Expected: 95%+ accuracy in 1-2 hours
- Much easier than 101-class classification
"""
def __init__(self, data_root, annotation_path, target_action="Basketball",
frames_per_clip=16, train=True):
self.target_action = target_action
self.target_class_idx = UCF101_CLASSES.index(target_action)
print(f"🎯 Creating Binary Classifier: '{target_action}' vs All Others")
print(f"📍 Target class index: {self.target_class_idx}")
# Load full UCF101 dataset
self.dataset = datasets.UCF101(
root=data_root,
annotation_path=annotation_path,
frames_per_clip=frames_per_clip,
step_between_clips=1,
train=train,
output_format="THWC"
)
self.transform = VideoTransform()
# Count target vs non-target samples
self.target_samples = 0
self.non_target_samples = 0
for i in range(len(self.dataset)):
_, _, label = self.dataset[i]
if label == self.target_class_idx:
self.target_samples += 1
else:
self.non_target_samples += 1
print(f"🏀 {target_action} samples: {self.target_samples}")
print(f"🚫 Non-{target_action} samples: {self.non_target_samples}")
print(f"📊 Total samples: {len(self.dataset)}")
# Calculate class weights for balanced training
total = self.target_samples + self.non_target_samples
self.target_weight = total / (2 * self.target_samples)
self.non_target_weight = total / (2 * self.non_target_samples)
print(f"⚖️ Class weights - {target_action}: {self.target_weight:.3f}, Others: {self.non_target_weight:.3f}")
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
try:
video, audio, original_label = self.dataset[idx]
# Convert to binary label: 1 = target action, 0 = all others
binary_label = 1 if original_label == self.target_class_idx else 0
return self.transform(video), binary_label
except Exception as e:
# Return dummy data on error
if idx < 5: # Only print first few errors
print(f"⚠️ Error loading sample {idx}: {e}")
return self.transform(None), 0
def get_binary_datasets(data_root, annotation_path, target_action="Basketball"):
"""
Create binary classification datasets for a specific action
Returns:
train_dataset: Binary training dataset (target_action vs others)
test_dataset: Binary test dataset (target_action vs others)
"""
print(f"\n🚀 Creating Binary Datasets for: {target_action}")
print("=" * 50)
train_dataset = BinaryActionDataset(
data_root, annotation_path, target_action=target_action, train=True
)
test_dataset = BinaryActionDataset(
data_root, annotation_path, target_action=target_action, train=False
)
return train_dataset, test_dataset
def get_binary_dataloaders(data_root, annotation_path, target_action="Basketball",
batch_size=8, num_workers=0):
"""
Get binary classification dataloaders ready for training
Returns:
train_loader, test_loader, class_weights
"""
train_dataset, test_dataset = get_binary_datasets(data_root, annotation_path, target_action)
# Create DataLoaders
train_loader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True,
num_workers=num_workers, pin_memory=True
)
test_loader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=False,
num_workers=num_workers, pin_memory=True
)
# Return class weights for balanced loss
class_weights = torch.tensor([
train_dataset.non_target_weight, # Class 0 (not target)
train_dataset.target_weight # Class 1 (target)
])
print(f"\n📦 DataLoaders created:")
print(f" 🎯 Train batches: {len(train_loader)}")
print(f" 🎯 Test batches: {len(test_loader)}")
print(f" ⚖️ Class weights: {class_weights}")
return train_loader, test_loader, class_weights
# Available actions for binary classification
SPORTS_ACTIONS = [
'Basketball', 'BasketballDunk', 'TennisSwing', 'GolfSwing', 'SoccerJuggling',
'SoccerPenalty', 'BaseballPitch', 'Bowling', 'TableTennisShot', 'VolleyballSpiking'
]
EXERCISE_ACTIONS = [
'PushUps', 'PullUps', 'Lunges', 'BodyWeightSquats', 'JumpingJack',
'WallPushups', 'HandstandPushups', 'BenchPress', 'CleanAndJerk'
]
MUSIC_ACTIONS = [
'PlayingGuitar', 'PlayingPiano', 'PlayingViolin', 'PlayingFlute',
'PlayingCello', 'Drumming', 'PlayingSitar', 'PlayingTabla', 'PlayingDaf', 'PlayingDhol'
]
if __name__ == "__main__":
# Test basketball binary classification
data_root = r"C:\ASH_PROJECT\data\UCF101"
annotation_path = r"C:\ASH_PROJECT\data\ucfTrainTestlist"
print("🧪 Testing Basketball Binary Classification Dataset...")
train_loader, test_loader, class_weights = get_binary_dataloaders(
data_root, annotation_path, target_action="Basketball", batch_size=4
)
# Test a few batches
print("\n🔍 Testing data loading...")
for i, (clips, labels) in enumerate(train_loader):
print(f"Batch {i+1}: clips={clips.shape}, labels={labels}")
if i >= 2: # Test only first 3 batches
break
print("✅ Binary dataset test completed!") |