Upload files to 'EVM ohne Buffer'

EVM ohne Buffer/eulerian.py

+import numpy as np
+import scipy.fftpack as fftpack
+
+
+# Temporal bandpass filter with Fast-Fourier Transform
+def fft_filter(video, freq_min, freq_max, fps):
+    fft = fftpack.fft(video, axis=0)
+    frequencies = fftpack.fftfreq(video.shape[0], d=1.0 / fps)
+    bound_low = (np.abs(frequencies - freq_min)).argmin()
+    bound_high = (np.abs(frequencies - freq_max)).argmin()
+    fft[:bound_low] = 0
+    fft[bound_high:-bound_high] = 0
+    fft[-bound_low:] = 0
+    iff = fftpack.ifft(fft, axis=0)
+    result = np.abs(iff)
+    result *= 100  # Amplification factor
+
+    return result, fft, frequencies

EVM ohne Buffer/heartrate.py

+from scipy import signal
+
+
+# Calculate heart rate from FFT peaks
+def find_heart_rate(fft, freqs, freq_min, freq_max):
+    fft_maximums = []
+
+    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)
+
+    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
+
+    return freqs[max_peak] * 60

EVM ohne Buffer/main.py

+from collections import deque
+import threading
+import time
+
+import cv2
+import pyramids
+import heartrate
+import preprocessing
+import eulerian
+import numpy as np
+
+class main():
+    def __init__(self):
+        # Frequency range for Fast-Fourier Transform
+        self.freq_min = 1
+        self.freq_max = 5
+        self.BUFFER_LEN = 10
+        self.BUFFER = deque(maxlen=self.BUFFER_LEN)
+        self.FPS_BUFFER = deque(maxlen=self.BUFFER_LEN)
+        self.buffer_lock = threading.Lock()
+        self.FPS = []
+
+    def video(self):
+        cap = cv2.VideoCapture(0)
+
+        while   len(self.BUFFER) < self.BUFFER_LEN:
+            start_time = time.time()
+            ret, frame = cap.read()
+            frame = cv2.resize(frame, (500, 500))
+            self.BUFFER.append(frame)
+            stop_time = time.time()
+            self.FPS_BUFFER.append(stop_time-start_time)
+        self.FPS = round(1 / np.mean(np.array(self.FPS_BUFFER)))
+
+        print("Buffer ready")
+
+
+        while   True:
+            start_time = time.time()
+            ret, frame = cap.read()
+            frame = cv2.resize(frame, (500, 500))
+            self.BUFFER.append(frame)
+            stop_time = time.time()
+            self.FPS_BUFFER.append(stop_time-start_time)
+            #threading.Event().wait(0.02)
+            self.FPS = round(1 / np.mean(np.array(self.FPS_BUFFER)))
+    
+
+
+    def processing(self):
+    # Build Laplacian video pyramid
+        while True:
+            with self.buffer_lock:
+                PROCESS_BUFFER = np.array(self.BUFFER)
+            lap_video = pyramids.build_video_pyramid(PROCESS_BUFFER)
+
+            amplified_video_pyramid = []
+
+            for i, video in enumerate(lap_video):
+                if i == 0 or i == len(lap_video)-1:
+                    continue
+
+            # Eulerian magnification with temporal FFT filtering
+            result, fft, frequencies = eulerian.fft_filter(video, self.freq_min, self.freq_max, self.FPS)
+            lap_video[i] += result
+
+            # Calculate heart rate
+            heart_rate = heartrate.find_heart_rate(fft, frequencies, self.freq_min, self.freq_max)
+
+            # Collapse laplacian pyramid to generate final video
+            #amplified_frames = pyramids.collapse_laplacian_video_pyramid(lap_video, len(self.BUFFER))
+
+            # Output heart rate and final video
+            print("Heart rate: ", heart_rate, "bpm")
+
+            threading.Event().wait(2)
+        
+
+
+if __name__ == '__main__':
+    MAIN = main()
+    
+    video_thread = threading.Thread(target=MAIN.video)
+    processing_thread = threading.Thread(target=MAIN.processing)
+
+    # Starte die Threads
+    video_thread.start()
+    time.sleep(2)
+    print("__SYNCING___")
+    processing_thread.start()
+    
+
+
+
+

EVM ohne Buffer/preprocessing.py

+import cv2
+import numpy as np
+
+faceCascade = cv2.CascadeClassifier("haarcascades/haarcascade_frontalface_alt0.xml")
+
+
+# Read in and simultaneously preprocess video
+def read_video(path):
+    cap = cv2.VideoCapture(path)
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    video_frames = []
+    face_rects = ()
+
+    while cap.isOpened():
+        ret, img = cap.read()
+        if not ret:
+            break
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        roi_frame = img
+
+        # Detect face
+        if len(video_frames) == 0:
+            face_rects = faceCascade.detectMultiScale(gray, 1.3, 5)
+
+        # Select ROI
+        if len(face_rects) > 0:
+            for (x, y, w, h) in face_rects:
+                roi_frame = img[y:y + h, x:x + w]
+            if roi_frame.size != img.size:
+                roi_frame = cv2.resize(roi_frame, (500, 500))
+                frame = np.ndarray(shape=roi_frame.shape, dtype="float")
+                frame[:] = roi_frame * (1. / 255)
+                video_frames.append(frame)
+
+    frame_ct = len(video_frames)
+    cap.release()
+
+    return video_frames, frame_ct, fps

EVM ohne Buffer/pyramids.py

+import cv2
+import numpy as np
+
+
+# Build Gaussian image pyramid
+def build_gaussian_pyramid(img, levels):
+    float_img = np.ndarray(shape=img.shape, dtype="float")
+    float_img[:] = img
+    pyramid = [float_img]
+
+    for i in range(levels-1):
+        float_img = cv2.pyrDown(float_img)
+        pyramid.append(float_img)
+
+    return pyramid
+
+
+# Build Laplacian image pyramid from Gaussian pyramid
+def build_laplacian_pyramid(img, levels):
+    gaussian_pyramid = build_gaussian_pyramid(img, levels)
+    laplacian_pyramid = []
+
+    for i in range(levels-1):
+        upsampled = cv2.pyrUp(gaussian_pyramid[i+1])
+        (height, width, depth) = upsampled.shape
+        gaussian_pyramid[i] = cv2.resize(gaussian_pyramid[i], (height, width))
+        diff = cv2.subtract(gaussian_pyramid[i],upsampled)
+        laplacian_pyramid.append(diff)
+
+    laplacian_pyramid.append(gaussian_pyramid[-1])
+
+    return laplacian_pyramid
+
+
+# Build video pyramid by building Laplacian pyramid for each frame
+def build_video_pyramid(frames):
+    lap_video = []
+
+    for i, frame in enumerate(frames):
+        pyramid = build_laplacian_pyramid(frame, 3)
+        for j in range(3):
+            if i == 0:
+                lap_video.append(np.zeros((len(frames), pyramid[j].shape[0], pyramid[j].shape[1], 3)))
+            lap_video[j][i] = pyramid[j]
+
+    return lap_video
+
+
+# Collapse video pyramid by collapsing each frame's Laplacian pyramid
+def collapse_laplacian_video_pyramid(video, frame_ct):
+    collapsed_video = []
+
+    for i in range(frame_ct):
+        prev_frame = video[-1][i]
+
+        for level in range(len(video) - 1, 0, -1):
+            pyr_up_frame = cv2.pyrUp(prev_frame)
+            (height, width, depth) = pyr_up_frame.shape
+            prev_level_frame = video[level - 1][i]
+            prev_level_frame = cv2.resize(prev_level_frame, (height, width))
+            prev_frame = pyr_up_frame + prev_level_frame
+
+        # Normalize pixel values
+        min_val = min(0.0, prev_frame.min())
+        prev_frame = prev_frame + min_val
+        max_val = max(1.0, prev_frame.max())
+        prev_frame = prev_frame / max_val
+        prev_frame = prev_frame * 255
+
+        prev_frame = cv2.convertScaleAbs(prev_frame)
+        collapsed_video.append(prev_frame)
+
+    return collapsed_video