def track(i=0):
cap = cv2.VideoCapture(i)
cap.set(cv2.cv.CV_CAP_PROP_FPS, 10)
kernel = np.ones((5,5),np.uint8)
out = False
while not out:
a, img = cap.read()
# ======================================================
# Detecta las caras de la imagen y devuelve
# las esquinas de los rectangulos q las contienen
# ======================================================
puntos = dtct.face(img)
try:
# ================================================================
# Set the coor properly so the faces are correctly extracted
# ================================================================
faces = []
for pto in puntos:
P1 = pto[0:2]
P2 = pto[2:]
f = img[P1[1]:P2[1], P1[0]:P2[0]]
faces.append(f)
# ==========================================================
# Rescale the faces and show them in upper left corner
# ==========================================================
cont = 0
for face in faces:
n = 70
face = cv2.resize(face,(n, n), interpolation = cv2.INTER_AREA)
img[0:n ,n*cont:n*(cont+1)] = face
cont += 1
except IndexError:
pass # if there are no faces
# ==================================
# Draws a box around each face
# ==================================
dtct.box(puntos, img)
# ===============================
# Shows the processed image
# ===============================
cv2.imshow('face',img)
k = cv2.waitKey(5) & 0xFF # Waits for <Esc> key
if k == 27: # to exit the infinite loop
out = True #
# break #
cv2.destroyAllWindows()
def track(i=0):
cap = cv2.VideoCapture(i)
cap.set(cv2.cv.CV_CAP_PROP_FPS, 10)
kernel = np.ones((5,5),np.uint8)
plt.ion()
plt.show()
fig = plt.figure()
ax = fig.add_subplot(111)
out = False
Npersonas=0
while not out:
a, img = cap.read()
# ======================================================
# Detecta las caras de la imagen y devuelve
# las esquinas de los rectangulos q las contienen
# ======================================================
puntos = dtct.face(img)
try:
# ==================================
# get the centers of the faces
# ==================================
centros = dtct.center(puntos,img)
except IndexError:
pass # if there are no faces
# ==================================
# Draws a box around each face
# ==================================
dtct.box(puntos, img)
# ==================================
# Draws a box around each face
# ==================================
dtct.box(puntos, img)
# ===============================
# Shows the processed image
# ===============================
cv2.imshow('face',img)
k = cv2.waitKey(5) & 0xFF # Waits for <Esc> key
if k == 27: # to exit the infinite loop
out = True #
# break #
# =====================================================
# Prepares the data to be plotted with matplotlib
# =====================================================
X, Y = [], []
for centro in centros:
X.append(centro[0])
Y.append(centro[1])
ax.plot(X,Y,'ro',markersize=10)
ax.axis([0, img.shape[0], 0, img.shape[1]])
fig.canvas.draw()
ax.cla()
if len(centros) > Npersonas:
Npersonas = len(centros)
cv2.destroyAllWindows()
flip = False
out = False
pos_x = '90'
pos_y = '90'
prev_angle = 90
while not out:
a, img = cap.read()
if flip: img = cv2.flip(img, 0)
vid_y, vid_x, _ = img.shape
# Look for faces
centers, corners = dtct.face(img)
# Draw a box around each face
dtct.box(corners, img)
if len(centers) > 0:
# Find centroid of all the faces (weighted by the size of the face)
M = np.array(centers)
X = M[:, 0]
Y = M[:, 1]
A = M[:, 2]
x0 = np.average(X, weights=A) # position in pixels
y0 = np.average(Y, weights=A) #
x0_deg = 180 - (170 * (x0 / vid_x) + 10)
x0_deg = (0.5 - (x0 / vid_x)) * 2 # \in [-1,1]
if not flip: x0_deg *= -1
if abs(x0_deg) > 0.05:
x0_deg = 5 * x0_deg #/abs(x0_deg)
