Fix heart-rate extraction: robust peak detection and safe returnHEADmaster

3 files changed, 271 insertions, 20 deletions

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new file mode 100644
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+++ b/.gitignore
@@ -0,0 +1,228 @@
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diff --git a/heartrate.py b/heartrate.py
index 2e9e0c7..0e77df7 100644
--- a/heartrate.py
+++ b/heartrate.py
@@ -2,23 +2,26 @@ from scipy import signal
 
 # Calculate heart rate from FFT peaks
 def find_heart_rate(fft, freqs, freq_min, freq_max):
-    fft_maximums = []
+    import numpy as np
 
+    # Build amplitude array for frequency bins within range
+    fft_maximums = np.zeros(fft.shape[0], dtype=np.float32)
     for i in range(fft.shape[0]):
         if freq_min <= freqs[i] <= freq_max:
-            fftMap = abs(fft[i])
-            fft_maximums.append(fftMap.max())
-        else:
-            fft_maximums.append(0)
+            fft_map = np.abs(fft[i])
+            # maximum amplitude across spatial channels
+            fft_maximums[i] = float(fft_map.max())
 
+    # Find peaks in the amplitude spectrum
     peaks, properties = signal.find_peaks(fft_maximums)
-    max_peak = -1
-    max_freq = 0
 
-    # Find frequency with max amplitude in peaks
-    for peak in peaks:
-        if fft_maximums[peak] > max_freq:
-            max_freq = fft_maximums[peak]
-            max_peak = peak
+    if len(peaks) == 0:
+        # No clear peak found
+        return None
 
-    return freqs[max_peak] * 60
+    # Select peak with maximum amplitude
+    peak_amplitudes = fft_maximums[peaks]
+    best_idx = peaks[np.argmax(peak_amplitudes)]
+
+    # Convert frequency (Hz) to beats per minute
+    return float(freqs[best_idx]) * 60.0
diff --git a/main.py b/main.py
index 623e5af..5a626be 100644
--- a/main.py
+++ b/main.py
@@ -12,9 +12,9 @@ freq_min = 1
 freq_max = 1.8
 
 # Mode: 'heartrate' or 'deepfake'
-MODE = 'deepfake'  # Change to 'heartrate' for original functionality
+MODE = 'heartrate'  # Change to 'heartrate' for original functionality
 
-video_path = '/Users/aloshdenny/Downloads/videoplayback.mp4'
+video_path = '/home/rishi/devmt/deepfake-eulerian-video-magnification/20251106_152546.mp4'
 
 # Preprocessing phase - optimized to reduce memory
 print("Reading + preprocessing video...")
@@ -55,11 +55,22 @@ for i, video in enumerate(lap_video):
     result, fft, frequencies = eulerian.fft_filter(video, freq_min, freq_max, fps)
     lap_video[i] += result
     
+    # Compute heart rate from middle pyramid level when in heartrate mode
+    if MODE == 'heartrate' and i == 1:
+        try:
+            heart_rate = heartrate.find_heart_rate(fft, frequencies, freq_min, freq_max)
+            if heart_rate is None:
+                print(f"Warning: could not estimate heart rate from level {i}")
+            else:
+                print(f"Estimated heart rate (from level {i}): {heart_rate:.2f} bpm")
+        except Exception as e:
+            print("Warning: failed to compute heart rate:", e)
+
     # Store FFT data for deepfake detection (only for middle level to save memory)
     if i == 1 and MODE == 'deepfake':
         all_fft.append(fft)
         all_frequencies.append(frequencies)
-    
+
     # Free intermediate results
     del result, fft, frequencies
     gc.collect()
@@ -72,14 +83,19 @@ if MODE == 'deepfake' and len(all_fft) > 0:
         freq_max=freq_max,
         confidence_threshold=0.6
     )
+    # Keep a reference to the FFT/frequencies used for visualization
+    saved_fft = all_fft[0]
+    saved_frequencies = all_frequencies[0]
+
     detection_result = detector.detect(
-        all_fft[0], 
-        all_frequencies[0], 
+        saved_fft, 
+        saved_frequencies, 
         freq_min, 
         freq_max,
         video_frames=None,  # Don't pass video_frames to save memory
         detailed=True
     )
+    # Free large arrays after creating saved references
     del all_fft, all_frequencies
     gc.collect()
 
@@ -97,8 +113,12 @@ elif MODE == 'deepfake':
     try:
         import matplotlib
         matplotlib.use('TkAgg')  # or 'Qt5Agg' depending on your system
-        detector.visualize_detection(fft, frequencies, detection_result, 
-                                    save_path='deepfake_analysis.png')
+        # Use saved FFT/frequencies from detection if available
+        if 'saved_fft' in locals() and 'saved_frequencies' in locals():
+            detector.visualize_detection(saved_fft, saved_frequencies, detection_result, 
+                                        save_path='deepfake_analysis.png')
+        else:
+            print("No FFT data available for visualization")
     except ImportError:
         print("Matplotlib not available for visualization")