emotionDetector.py

from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
import argparse
import imutils
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-c", "--cascade", required = True)
ap.add_argument("-m", "--model", required = True)
ap.add_argument("-v", "--video")
args = vars(ap.parse_args())

detector = cv2.CascadeClassifier(args["cascade"])
model = load_model(args["model"])
EMOTIONS = ["angry", "scared", "happy", "sad", "surprised", "neutral"]

if not args.get("video", False):
    camera = cv2.VideoCapture(0)

else:
    camera = cv2.VideoCapture(args["video"])

while True:
    (grabbed, frame) = camera.read()

    if args.get("video") and not grabbed:
        break

    frame = imutils.resize(frame, width = 300)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    canvas = np.zeros((220, 300, 3), dtype = "uint8")
    frameClone = frame.copy()

    rects = detector.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 5, minSize = (30, 30), flags = cv2.CASCADE_SCALE_IMAGE)

    if len(rects) > 0:
        rect = sorted(rects, reverse = True, key = lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
        (fX, fY, fW, fH) = rect

        roi = gray[fY:fY + fH, fX:fX + fW]
        roi = cv2.resize(roi, (48, 48))
        roi = roi.astype("float") / 255.0
        roi = img_to_array(roi)
        roi = np.expand_dims(roi ,axis = 0)
        
        preds = model.predict(roi)[0]
        label = EMOTIONS[preds.argmax()]
        
        if label == "neutral":
            canvas2 = cv2.imread("neutral.png")
        
        elif label == "angry":
            canvas2 = cv2.imread("angry.png")

        elif label == "scared":
            canvas2 = cv2.imread("scared.png")

        elif label == "happy":
            canvas2 = cv2.imread("happy.png")

        elif label == "sad":
            canvas2 = cv2.imread("sad.png")

        elif label == "surprised":
            canvas2 = cv2.imread("surprised.png")

        for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
            text = "{}: {:.2f}%".format(emotion, prob * 100)

            w = int(prob * 300)
            cv2.rectangle(canvas, (5, (i * 35) + 5), (w, (i * 35) + 35), (0, 0, 255), -1)
            cv2.putText(canvas, text, (10, (i * 35) + 23), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 2)

            cv2.putText(frameClone, label, (fX, fY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
            cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH), (0, 0, 255), 2)

        cv2.imshow("Face", frameClone)
        cv2.imshow("Probabilities", canvas)
        cv2.imshow("eb", canvas2)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

camera.release()
cv2.destroyAllWindows()