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Kieran
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18
eulerian.py
18
eulerian.py
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import numpy as np
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import scipy.fftpack as fftpack
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# Temporal bandpass filter with Fast-Fourier Transform
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def fft_filter(video, freq_min, freq_max, fps):
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fft = fftpack.fft(video, axis=0)
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frequencies = fftpack.fftfreq(video.shape[0], d=1.0 / fps)
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bound_low = (np.abs(frequencies - freq_min)).argmin()
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bound_high = (np.abs(frequencies - freq_max)).argmin()
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fft[:bound_low] = 0
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fft[bound_high:-bound_high] = 0
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fft[-bound_low:] = 0
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iff = fftpack.ifft(fft, axis=0)
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result = np.abs(iff)
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result *= 100 # Amplification factor
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return result, fft, frequencies
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25
heartrate.py
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heartrate.py
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from scipy import signal
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# Calculate heart rate from FFT peaks
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def find_heart_rate(fft, freqs, freq_min, freq_max):
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fft_maximums = []
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for i in range(fft.shape[0]):
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if freq_min <= freqs[i] <= freq_max:
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fftMap = abs(fft[i])
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fft_maximums.append(fftMap.max())
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else:
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fft_maximums.append(0)
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peaks, properties = signal.find_peaks(fft_maximums)
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max_peak = -1
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max_freq = 0
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# Find frequency with max amplitude in peaks
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for peak in peaks:
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if fft_maximums[peak] > max_freq:
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max_freq = fft_maximums[peak]
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max_peak = peak
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return freqs[max_peak] * 60
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95
main.py
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main.py
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from collections import deque
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import threading
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import time
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import cv2
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import pyramids
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import heartrate
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import preprocessing
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import eulerian
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import numpy as np
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class main():
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def __init__(self):
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# Frequency range for Fast-Fourier Transform
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self.freq_min = 1
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self.freq_max = 5
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self.BUFFER_LEN = 10
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self.BUFFER = deque(maxlen=self.BUFFER_LEN)
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self.FPS_BUFFER = deque(maxlen=self.BUFFER_LEN)
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self.buffer_lock = threading.Lock()
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self.FPS = []
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def video(self):
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cap = cv2.VideoCapture(0)
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while len(self.BUFFER) < self.BUFFER_LEN:
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start_time = time.time()
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ret, frame = cap.read()
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frame = cv2.resize(frame, (500, 500))
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self.BUFFER.append(frame)
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stop_time = time.time()
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self.FPS_BUFFER.append(stop_time-start_time)
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self.FPS = round(1 / np.mean(np.array(self.FPS_BUFFER)))
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print("Buffer ready")
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while True:
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start_time = time.time()
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ret, frame = cap.read()
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frame = cv2.resize(frame, (500, 500))
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self.BUFFER.append(frame)
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stop_time = time.time()
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self.FPS_BUFFER.append(stop_time-start_time)
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#threading.Event().wait(0.02)
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self.FPS = round(1 / np.mean(np.array(self.FPS_BUFFER)))
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def processing(self):
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# Build Laplacian video pyramid
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while True:
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with self.buffer_lock:
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PROCESS_BUFFER = np.array(self.BUFFER)
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lap_video = pyramids.build_video_pyramid(PROCESS_BUFFER)
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amplified_video_pyramid = []
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for i, video in enumerate(lap_video):
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if i == 0 or i == len(lap_video)-1:
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continue
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# Eulerian magnification with temporal FFT filtering
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result, fft, frequencies = eulerian.fft_filter(video, self.freq_min, self.freq_max, self.FPS)
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lap_video[i] += result
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# Calculate heart rate
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heart_rate = heartrate.find_heart_rate(fft, frequencies, self.freq_min, self.freq_max)
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# Collapse laplacian pyramid to generate final video
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#amplified_frames = pyramids.collapse_laplacian_video_pyramid(lap_video, len(self.BUFFER))
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# Output heart rate and final video
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print("Heart rate: ", heart_rate, "bpm")
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threading.Event().wait(2)
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if __name__ == '__main__':
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MAIN = main()
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video_thread = threading.Thread(target=MAIN.video)
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processing_thread = threading.Thread(target=MAIN.processing)
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# Starte die Threads
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video_thread.start()
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time.sleep(2)
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print("__SYNCING___")
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processing_thread.start()
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import cv2
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import numpy as np
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faceCascade = cv2.CascadeClassifier("haarcascades/haarcascade_frontalface_alt0.xml")
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# Read in and simultaneously preprocess video
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def read_video(path):
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cap = cv2.VideoCapture(path)
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fps = int(cap.get(cv2.CAP_PROP_FPS))
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video_frames = []
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face_rects = ()
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while cap.isOpened():
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ret, img = cap.read()
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if not ret:
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break
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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roi_frame = img
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# Detect face
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if len(video_frames) == 0:
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face_rects = faceCascade.detectMultiScale(gray, 1.3, 5)
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# Select ROI
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if len(face_rects) > 0:
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for (x, y, w, h) in face_rects:
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roi_frame = img[y:y + h, x:x + w]
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if roi_frame.size != img.size:
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roi_frame = cv2.resize(roi_frame, (500, 500))
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frame = np.ndarray(shape=roi_frame.shape, dtype="float")
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frame[:] = roi_frame * (1. / 255)
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video_frames.append(frame)
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frame_ct = len(video_frames)
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cap.release()
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return video_frames, frame_ct, fps
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73
pyramids.py
73
pyramids.py
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import cv2
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import numpy as np
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# Build Gaussian image pyramid
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def build_gaussian_pyramid(img, levels):
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float_img = np.ndarray(shape=img.shape, dtype="float")
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float_img[:] = img
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pyramid = [float_img]
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for i in range(levels-1):
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float_img = cv2.pyrDown(float_img)
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pyramid.append(float_img)
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return pyramid
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# Build Laplacian image pyramid from Gaussian pyramid
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def build_laplacian_pyramid(img, levels):
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gaussian_pyramid = build_gaussian_pyramid(img, levels)
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laplacian_pyramid = []
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for i in range(levels-1):
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upsampled = cv2.pyrUp(gaussian_pyramid[i+1])
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(height, width, depth) = upsampled.shape
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gaussian_pyramid[i] = cv2.resize(gaussian_pyramid[i], (height, width))
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diff = cv2.subtract(gaussian_pyramid[i],upsampled)
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laplacian_pyramid.append(diff)
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laplacian_pyramid.append(gaussian_pyramid[-1])
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return laplacian_pyramid
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# Build video pyramid by building Laplacian pyramid for each frame
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def build_video_pyramid(frames):
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lap_video = []
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for i, frame in enumerate(frames):
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pyramid = build_laplacian_pyramid(frame, 3)
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for j in range(3):
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if i == 0:
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lap_video.append(np.zeros((len(frames), pyramid[j].shape[0], pyramid[j].shape[1], 3)))
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lap_video[j][i] = pyramid[j]
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return lap_video
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# Collapse video pyramid by collapsing each frame's Laplacian pyramid
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def collapse_laplacian_video_pyramid(video, frame_ct):
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collapsed_video = []
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for i in range(frame_ct):
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prev_frame = video[-1][i]
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for level in range(len(video) - 1, 0, -1):
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pyr_up_frame = cv2.pyrUp(prev_frame)
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(height, width, depth) = pyr_up_frame.shape
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prev_level_frame = video[level - 1][i]
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prev_level_frame = cv2.resize(prev_level_frame, (height, width))
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prev_frame = pyr_up_frame + prev_level_frame
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# Normalize pixel values
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min_val = min(0.0, prev_frame.min())
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prev_frame = prev_frame + min_val
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max_val = max(1.0, prev_frame.max())
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prev_frame = prev_frame / max_val
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prev_frame = prev_frame * 255
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prev_frame = cv2.convertScaleAbs(prev_frame)
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collapsed_video.append(prev_frame)
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return collapsed_video
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