Added graphic saving and parser

README.md

@@ -1,6 +1,9 @@
+Como leer el nombre de los  archivos con graficos:
+`{Nombre del video del que salieron}_{Tamaño de la ventana}.png`
 # ITBA - Métodos Numéricos Avanzados - Grupo 10
 Second Advanced Numerical Methods Project: Hear Beat Rate
 
+
 ## Getting Started
 These instructions will install the development environment into your local machine.
 

fast_fourier_transform.py

@@ -8,7 +8,7 @@ def FFT_R(x):
     splitN = int(N/2)
 
     if np.log2(N) % 1 > 0:
-        raise ValueError('values count must be a power of 2, "{}" given.'.format(values_count))
+        raise ValueError('values count must be a power of 2, "{}" given.'.format(N))
     elif N < 2:
         return x
     else:

given-data/heartrate.py

@@ -1,4 +1,4 @@
-b# -*- coding: utf-8 -*-
+# -*- coding: utf-8 -*-
 """
 Created on Sat Sep 16 19:23:10 2017
 
@@ -52,15 +52,16 @@
 G = np.abs(np.fft.fftshift(np.fft.fft(g)))**2
 B = np.abs(np.fft.fftshift(np.fft.fft(b)))**2
 
-plt.plot(60*f,R)
+plt.plot(60*f,R,'red')
 plt.xlim(0,200)
 
 
-plt.plot(60*f,G)
+plt.plot(60*f,G,'green')
 plt.xlim(0,200)
 plt.xlabel("frecuencia [1/minuto]")
 
-plt.plot(60*f,B)
+plt.plot(60*f,B,'blue')
 plt.xlim(0,200)
+plt.show()
 
 print("Frecuencia cardíaca: ", abs(f[np.argmax(G)])*60, " pulsaciones por minuto")

main.py

@@ -0,0 +1,70 @@
+import argparse
+import numpy as np
+import utils as ut
+import matplotlib.pyplot as plt
+import cv2
+import fast_fourier_transform as fft
+
+parser = argparse.ArgumentParser(description="Beats per minute monitor using FFT by analyzing a video")
+parser.add_argument("video", help="Video to analyze", type=str)
+parser.add_argument("--size", "-s", help="Size of observed window. It is a square in the center of the screen.",
+        type=int, default=30)
+parser.add_argument("--npfft",help="Use numpy's fft method if True, use custom method if False",type=bool,default=True)
+
+args = parser.parse_args()
+
+cap = cv2.VideoCapture(args.video)
+length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+length = int(2 ** np.floor(np.log2(length)))
+width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+fps    = cap.get(cv2.CAP_PROP_FPS)
+
+size = args.size
+upperLeftCornerX = width // 2 - size // 2 
+upperLeftCornerY = height // 2 - size // 2 
+lowerRightCornerX = width // 2 + size // 2 
+lowerRightCornerY = height// 2 + size // 2 
+
+r, g, b = ut.load_frames(cap,upperLeftCornerX,upperLeftCornerY,lowerRightCornerX,lowerRightCornerY,length)
+cap.release()
+cv2.destroyAllWindows()
+
+n = length
+f = np.linspace(-n/2,n/2-1,n)*fps/n
+r = r[0,0:n]-np.mean(r[0,0:n])
+g = g[0,0:n]-np.mean(g[0,0:n])
+b = b[0,0:n]-np.mean(b[0,0:n])
+
+fft_method = None
+if args.npfft:
+    fft_method = np.fft.fft
+else:
+    fft_method = fft.FFT_R
+
+R = np.abs(np.fft.fftshift(fft_method(r)))**2
+G = np.abs(np.fft.fftshift(fft_method(g)))**2
+B = np.abs(np.fft.fftshift(fft_method(b)))**2
+
+plt.subplot(2,1,1)
+plt.plot(60*f,R, 'red')
+plt.plot(60*f,G, 'green')
+plt.plot(60*f,B, 'blue')
+plt.xlim(0,200)
+plt.xlabel("frecuencia [1/minuto]")
+
+title = args.video.split("/")[-1].split(".")[0]
+plt.title(title)
+
+plt.subplot(2,1,2)
+plt.plot(np.arange(length),r, 'red')
+plt.plot(np.arange(length),g, 'green')
+plt.plot(np.arange(length),b, 'blue')
+plt.xlabel("valor r g b")
+plt.tight_layout()
+plt.savefig("./graphs/{}_{}.png".format(title, args.size))
+#plt.show()
+print("Frecuencia cardíaca: ", abs(f[np.argmax(R)])*60, " pulsaciones por minuto en R")
+print("Frecuencia cardíaca: ", abs(f[np.argmax(G)])*60, " pulsaciones por minuto en G")
+print("Frecuencia cardíaca: ", abs(f[np.argmax(B)])*60, " pulsaciones por minuto en B")
+

utils.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+def load_frames(cap,upperLeftCornerX,upperLeftCornerY,lowerRightCornerX,lowerRightCornerY,length):
+    r = np.zeros((1,length))
+    g = np.zeros((1,length))
+    b = np.zeros((1,length))
+
+    currentFrameNo = 0
+    while(cap.isOpened() and currentFrameNo < length):
+        ret, frame = cap.read()
+
+        if ret == True:
+            r[0,currentFrameNo] = np.mean(frame[upperLeftCornerX:lowerRightCornerX, upperLeftCornerY:lowerRightCornerY,0])
+            g[0,currentFrameNo] = np.mean(frame[upperLeftCornerX:lowerRightCornerX, upperLeftCornerY:lowerRightCornerY,1])
+            b[0,currentFrameNo] = np.mean(frame[upperLeftCornerX:lowerRightCornerX, upperLeftCornerY:lowerRightCornerY,2])
+        else:
+            break
+        currentFrameNo += 1
+    print(currentFrameNo)
+    return [r, g, b]
+