class Camera(object):
def __init__(self):
self.size = (640, 480)
# initialize the camera and grab a reference to the raw camera
# capture
self.camera = PiCamera()
self.camera.resolution = self.size
self.camera.framerate = 32
self.rawCapture = PiRGBArray(self.camera, size=self.size)
# construct the face detector and allow the camera to warm
# up
self.fd = FaceDetector("cascades/haarcascade_frontalface_default.xml")
time.sleep(0.1)
def show_camera(self):
# capture frames from the camera
for f in self.camera.capture_continuous(self.rawCapture,
format="bgr",
use_video_port=True):
# grab the raw NumPy array representing the image
self.frame = f.array
# resize the frame and convert it to grayscale
self.frame = self.rot180(imutils.resize(self.frame, width=300))
self.gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
# detect faces in the image and then clone the frame
# so that we can draw on it
self.faceRects = self.fd.detect(self.gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
self.frameClone = self.frame.copy()
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in self.faceRects:
cv2.rectangle(self.frameClone, (fX, fY), (fX + fW, fY + fH),
(0, 255, 0), 2)
# Desactivo el laser si detecto una cara
if len(self.faceRects) > 0:
GPIO.output(laser, 0)
else:
GPIO.output(laser, 1)
# show our detected faces, then clear the frame in
# preparation for the next frame
cv2.imshow("Robopot", self.frameClone)
self.rawCapture.truncate(0)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
def rot180(self, frame):
self.frame = frame
return numpy.rot90(numpy.rot90(self.frame))
# keep looping
while True:
if (os.path.exists(faceframe)):
fX = 0
fY = 0
fW = 0
fH = 0
# grab the current frame
print "reading image"
gray = cv2.imread('/dev/shm/face.jpg',0)
height, width = gray.shape #get resolution for plotting location to screen thirds in facedetect.py
# detect faces in the image and then clone the frame
# so that we can draw on it
print "making face detect rectangle"
faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 5,
minSize = (30, 30))
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in faceRects:
cv2.rectangle(gray, (fX, fY), (fX + fW, fY + fH), (0, 255, 0), 2)
print "Rectangle" + str(fX) + str(fY) + str(fW) + str(fH)
#Write rectangle to file for processing by facedetect.py
print "writing rectangle parameters to file"
if (os.path.exists(facelocation)):
os.remove(facelocation)
with open(facelocation,'w') as f:
f.write(str(fX)+",")
f.write(str(fY)+",")
f.write(str(fW)+",")
f.write(str(fH)+",")
f.write(str(height)+",")
# grab the raw NumPy array representing the image - ONLY FOR PI?
# frame = f.array
# resize the frame and convert it to grayscale
frame = cv2.flip(frame, 1)
frameorig = frame
frame = imutils.resize(frame, width=resizeTo)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
grayorig = cv2.cvtColor(frameorig, cv2.COLOR_BGR2GRAY)
# gray = cv2.GaussianBlur(gray, (21, 21), 0)
# detect faces in the image and then clone the frame
# so that we can draw on it
faceRects = fd.detect(gray,
scaleFactor=1.11,
minNeighbors=5,
minSize=(minValue, minValue))
frameClone = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB) #
# frameClone = frame.copy() # (the original line)
# frameClone = gray
overlay = np.zeros((screenHeight, screenWidth, 3),
np.uint8) # EXP overlay maybe for rects on fullres?
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in faceRects:
cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH), (0, 255, 0), 2)
fWi = int(fW)
fHi = int(fH)
tempArea = fWi * fHi
# print("Face area Temp is %s " % tempArea)
areaNumber = areaNumber + tempArea
# Create the inverted mask for the hat
orig_mask_inv = cv2.bitwise_not(orig_mask)
# Convert hat image to BGR
# and save the original image size (used later when re-sizing the image)
hat = hat_default[:,:,0:3]
orighatHeight, origHatWidth = hat.shape[:2]
while True:
ret, frame = cap.read()
frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = fd.detect(gray, 1.3, 5)
for(x,y,w,h) in faces:
hatWidth = w + int(w * 0.2)
hatHeight = hatWidth * origHatWidth / origHatWidth
hat = hat_default[:,:,0:3]
hat = cv2.resize(hat, (hatWidth,hatHeight), interpolation = cv2.INTER_AREA)
y1 = y - hatHeight + int(hatHeight*.3)
y2 = y + int(hatHeight*.3)
x1 = x + hatWidth*0.05
x2 = x1 + hatWidth
cropY = 0
while True: while True: # grab the current frame (grabbed, frame) = camera.read() # if we are viewing a video and we did not grab a # frame, then we have reached the end of the video if not grabbed: break # resize the frame and convert it to grayscale gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # detect faces in the image and then clone the frame # so that we can draw on it faceRects = fd.detect(gray, scaleFactor = 1.3, minNeighbors = 5,minSize = (100, 100)) frameClone = frame.copy() face = [] # loop over the face bounding boxes and draw them for (fX, fY, fW, fH) in faceRects: try: #find centroid of the rectangle cx = fX + int(fW/2) cy = fY + int(fH/2) fX -= int(fX/10*offset_constant) fY -= int(fY/10*offset_constant) fW += int(int(fX/10)*offset_constant) * 2 face = frameClone[fY:fY+int((fW*proportional_w)/proportional_h),fX:fX+fW] #resize face_resized = imutils.resize(face, width = larghezza_foto)
# Convert hat image to BGR
# and save the original image size (used later when re-sizing the image)
hat = hat_default[:, :, 0:3]
orighatHeight, origHatWidth = hat.shape[:2]
while True:
ret, frame = cap.read()
frame = cv2.resize(frame,
None,
fx=scaling_factor,
fy=scaling_factor,
interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = fd.detect(gray, 1.3, 5)
for (x, y, w, h) in faces:
hatWidth = w + int(w * 0.2)
hatHeight = hatWidth * origHatWidth / origHatWidth
hat = hat_default[:, :, 0:3]
hat = cv2.resize(hat, (hatWidth, hatHeight),
interpolation=cv2.INTER_AREA)
y1 = y - hatHeight + int(hatHeight * .3)
y2 = y + int(hatHeight * .3)
x1 = x + int(hatWidth * 0.05)
x2 = x1 + hatWidth