EVM Core Processor

from src.evm.evm_core import EVMProcessor

# Initialize processor with custom parameters
processor = EVMProcessor(levels=3, alpha_hr=30, alpha_rr=50)

# Process video frames
hr_signal, rr_signal = processor.process_dual_band(video_frames)

EVMProcessor(levels=LEVELS_RPI, alpha_hr=ALPHA_HR, alpha_rr=ALPHA_RR)

hr_signal, rr_signal = processor.process_dual_band(video_frames)

laplacian_pyramids = build_video_pyramid_stack(video_frames, levels=self.levels)

level_hr = min(3, num_levels - 1)  # HR: level 3 (higher spatial freq)
level_rr = min(2, num_levels - 1)  # RR: level 2 (lower spatial freq)

tensor_hr = extract_pyramid_level(laplacian_pyramids, level_hr)
tensor_rr = extract_pyramid_level(laplacian_pyramids, level_rr)

# HR band: 0.8-3 Hz (48-180 BPM)
filtered_tensor_hr = apply_temporal_bandpass(
    tensor_hr, LOW_HEART, HIGH_HEART, FPS, axis=0
)

# RR band: 0.2-0.8 Hz (12-48 RPM)
filtered_tensor_rr = apply_temporal_bandpass(
    tensor_rr, LOW_RESP, HIGH_RESP, FPS, axis=0
)

filtered_tensor_hr *= self.alpha_hr  # Amplify by 30x
filtered_tensor_rr *= self.alpha_rr  # Amplify by 50x

# HR: Green channel (best SNR for pulse)
signal_hr = extract_temporal_signal(filtered_tensor_hr, use_green_channel=True)

# RR: All channels average
signal_rr = extract_temporal_signal(filtered_tensor_rr, use_green_channel=True)

import numpy as np
from src.evm.evm_core import EVMProcessor

# Create processor with custom amplification
processor = EVMProcessor(
    levels=3,
    alpha_hr=25,  # Lower amplification for noisy videos
    alpha_rr=40
)

# Process video buffer
video_frames = [...]  # List of 200 BGR frames
hr_signal, rr_signal = processor.process_dual_band(video_frames)

if hr_signal is not None:
    print(f"HR signal shape: {hr_signal.shape}")  # (200,)
    print(f"HR signal range: {hr_signal.min():.2f} to {hr_signal.max():.2f}")
    
if rr_signal is not None:
    print(f"RR signal shape: {rr_signal.shape}")  # (200,)
    print(f"RR signal range: {rr_signal.min():.2f} to {rr_signal.max():.2f}")