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made separate functions for the main logic and delete unnecessary code #44

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made separate functions for the main logic and delete unnecessary code #44

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213 changes: 113 additions & 100 deletions FaceRecognizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,8 @@
import uuid
import rotateImage

# functions in face_utils module
'''
def rect_to_bb(rect):
# we will take the bounding box predicted by dlib library
# and convert it into (x, y, w, h) where x, y are coordinates
Expand All @@ -34,6 +36,7 @@ def shape_to_np(shape, dtype="int"):
coords[i] = (shape.part(i).x, shape.part(i).y)

return coords
'''

# construct the arguments

Expand Down Expand Up @@ -63,106 +66,116 @@ def shape_to_np(shape, dtype="int"):

}

# initialize dlib's face detector and facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])
face = FaceAligner(predictor, desiredFaceWidth=256)

# Load input image, resize and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect faces in the grayscale image
cv2.imshow("Input", image)
rects = detector(gray, 1)

# loop over the faces that are detected
for (i, rect) in enumerate(rects):
# Detected face landmark (x, y)-coordinates are converted into
# Numpy array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)

# convert dlib's rectangle to OpenCV bounding box and draw
# [i.e., (x, y, w, h)]
(x, y, w, h) = face_utils.rect_to_bb(rect)
faceOrig = imutils.resize(image[y:y + h, x:x + w], width=256)
faceAligned = face.align(image, gray, rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)


f = str(uuid.uuid4())
cv2.imwrite("foo/" + f + ".png", faceAligned)

# shows the face number
cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# loop over the (x, y) coordinate for facial landmark and drow on th image
for (x, y) in shape:
cv2.circle(image, (x, y), 1, (0, 0, 255), -1)

cv2.imshow("Original", faceOrig)
cv2.imshow("Aligned", faceAligned)
def processingImage(detector):
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect faces in the grayscale image
cv2.imshow("Input", image)
rects = detector(gray, 1)
processingDetectedFaces(image, gray, rects)


def processingDetectedFaces(image, gray, rects):
# loop over the faces that are detected
for (i, rect) in enumerate(rects):
# Detected face landmark (x, y)-coordinates are converted into
# Numpy array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)

# convert dlib's rectangle to OpenCV bounding box and draw
# [i.e., (x, y, w, h)]
(x, y, w, h) = face_utils.rect_to_bb(rect)
faceOrig = imutils.resize(image[y:y + h, x:x + w], width=256)
faceAligned = face.align(image, gray, rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

f = str(uuid.uuid4())
cv2.imwrite("foo/" + f + ".png", faceAligned)

# shows the face number
cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# loop over the (x, y) coordinate for facial landmark and drow on th image
for (x, y) in shape:
cv2.circle(image, (x, y), 1, (0, 0, 255), -1)

cv2.imshow("Original", faceOrig)
cv2.imshow("Aligned", faceAligned)
cv2.waitKey(0)

# show output with facial landmarks
cv2.imshow("Landmarks", image)

# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open(args["encodings"], "rb").read())

# load the input image and convert it from BGR to RGB
image = cv2.imread(args["image"])
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# detect the (x, y) coordinates of the bounding box corresponding to
# each face inthe input image and compute facial embeddings for each face
print("[INFO] recognizing faces...")
boxes = face_recognition.face_locations(rgb, model = args["detection_method"])
encodings = face_recognition.face_encodings(rgb, boxes)
embeddings(data, encodings, image, boxes)


def embeddings(data, encodings, image, boxes)
# initialize the list of names of detected faces
names = []

# loop over facial embeddings
for encoding in encodings:
# compares each face in the input image to our known encodings
matches = face_recognition.compare_faces(data["encodings"], encoding)
name = "Unknown"

# check if match is found or not
if True in matches:
#find the indexes of all matches and initialize a dictionary
# to count number of times a match occur
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}

# loop over matched indexes and maintain a count for each face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1

# Select the recognized face with maximum number of matches and
# if there is a tie Python selects first entry from the dictionary
name = max(counts, key=counts.get)

# update the list of names
names.append(name)
final_Process(image, boxes, names)


def final_Process(image, boxes, names)
# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# draw predicted face name on image
cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)

# Output Image
cv2.imshow("Detected face", image)
cv2.waitKey(0)
rotateImage.rotateFunction(image)


# show output with facial landmarks
cv2.imshow("Landmarks", image)

# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open(args["encodings"], "rb").read())

# load the input image and convert it from BGR to RGB
image = cv2.imread(args["image"])
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# detect the (x, y) coordinates of the bounding box corresponding to
# each face inthe input image and compute facial embeddings for each face
print("[INFO] recognizing faces...")
boxes = face_recognition.face_locations(rgb, model = args["detection_method"])
encodings = face_recognition.face_encodings(rgb, boxes)

# initialize the list of names of detected faces
names = []

# loop over facial embeddings
for encoding in encodings:
# compares each face in the input image to our known encodings
matches = face_recognition.compare_faces(data["encodings"], encoding)
name = "Unknown"

# check if match is found or not
if True in matches:
#find the indexes of all matches and initialize a dictionary
# to count number of times a match occur
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}

# loop over matched indexes and maintain a count for each face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1

# Select the recognized face with maximum number of matches and
# if there is a tie Python selects first entry from the dictionary
name = max(counts, key=counts.get)

# update the list of names
names.append(name)

# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# draw predicted face name on image
cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)

# Output Image

cv2.imshow("Detected face", image)
cv2.waitKey(0)

rotateImage.rotateFunction(image)
if __name__ == "__main__":
# initialize dlib's face detector and facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])
face = FaceAligner(predictor, desiredFaceWidth=256)
processingImage(detector)