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Update emotion_predictor #2

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56 changes: 50 additions & 6 deletions emotion_predictor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
detector = get_frontal_face_detector()
predictor = shape_predictor("/home/deeplearning/Desktop/shape_predictor_68_face_landmarks.dat")
clf = pickle.load(open("/home/deeplearning/PycharmProjects/project2/model.xml", "rb"))
clf = pickle.load(open("/home/deeplearning/PycharmProjects/project2/model.sav", "rb"))
emotions = ["happy", "anger", "fear", "sadness", "disgust", "surprise", "neutral"]


Expand All @@ -21,14 +21,17 @@ def get_landmarks(image):
xlist = []
ylist = []
for i in range(1,68):
cv2.circle(image, (shape.part(i).x, shape.part(i).y), 1, (0, 0, 255), thickness=2)
xlist.append(shape.part(i).x)
ylist.append(shape.part(i).y)
cv2.imshow("got landmarks", image)
cv2.waitKey(3000)
xmean = np.mean(xlist)
ymean = np.mean(ylist)
xcentral = [(x-xmean) for x in xlist]
ycentral = [(y-ymean) for y in ylist]
vectorized_landmarks = []
for x,y,w,z in zip(xcentral, ycentral, xlist, ylist):
for x, y, w, z in zip(xcentral, ycentral, xlist, ylist):
vectorized_landmarks.append(w)
vectorized_landmarks.append(z)
mean_array = np.asarray((ymean,xmean))
Expand All @@ -41,13 +44,54 @@ def get_landmarks(image):
data["landmark_vectorization"] = vectorized_landmarks


prediction_image = cv2.imread("/home/deeplearning/Desktop/face_recognition examples/biden.jpg") #input your image
prediction_image = cv2.imread("/home/deeplearning/Desktop/face_recognition examples/obama-720p.jpg")
gray = cv2.cvtColor(prediction_image, cv2.COLOR_BGR2GRAY)
clahe_image = clahe.apply(gray)
get_landmarks(clahe_image)
cv2.imshow("initial image", gray)
cv2.waitKey(3000)
faceDet = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_default.xml")
faceDet2 = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_alt2.xml")
faceDet3 = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_alt.xml")
faceDet4 = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_alt_tree.xml")
face = faceDet.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
face2 = faceDet2.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
face3 = faceDet3.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
face4 = faceDet4.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)

if len(face) == 1:
facefeatures = face
elif len(face2) == 1:
facefeatures = face2
elif len(face3) == 1:
facefeatures = face3
elif len(face4) == 1:
facefeatures = ""
for (x, y, w, h) in facefeatures: # Get coordinates and size of rectangle containing face
gray = gray[y:y+h, x:x+w] # Cut the frame to size
try:
out = cv2.resize(gray, (350, 350))
clahe_image = clahe.apply(out)
cv2.imshow("clahe image", clahe_image)
cv2.waitKey(4000)
get_landmarks(clahe_image)
except:
pass


if data["landmark_vectorization"] == "error":
print("no faces detected")
else:
prediction_data.append(data["landmark_vectorization"])
nparray = np.array(prediction_data)
print(clf.predict_proba(nparray))
final_results = clf.predict_proba(nparray)[0]
font = cv2.FONT_HERSHEY_SIMPLEX
initial_point = 100
for a,b in zip(emotions, final_results):
confidence_text = str(str(a) + " : " + str(b * 100) + " %")
cv2.putText(prediction_image, confidence_text, (30, initial_point), font, 1, (255,0,0), 1, cv2.LINE_AA)
initial_point = initial_point + 50
final_text = str("Emotion : " + str(emotions[int(np.argmax(final_results))]))
cv2.putText(prediction_image, final_text, (30, initial_point), font, 1, (255,0,0), 1, cv2.LINE_AA)
cv2.imshow("final output", prediction_image)
cv2.waitKey(6000)