Graphical_Visualisation.py

# Let us import the Libraries required.
import cv2
import numpy as np
import matplotlib.pyplot as plt

# From Module named "model", Let us import the FacialExpressionModel class.
from model import FacialExpressionModel

# Creating an instance of the class with the parameters as model and its weights.
test_model = FacialExpressionModel("model.json", "model_weights.h5")
facec = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Let us define a Function that does the Analysis of Emotions


def Emotion_Analysis(img):

    # Read the Image through OpenCv's imread()
    path = "static/" + str(img)
    img_p = cv2.imread(path)

    # Convert the Image into Gray Scale
    gray_fr = cv2.cvtColor(img_p, cv2.COLOR_BGR2GRAY)

    # Detect the Faces in the given Image and store it in faces.
    faces = facec.detectMultiScale(gray_fr, 1.5, 3)

    for (x, y, w, h) in faces:

        # Taking the Face part in the Image
        fc = gray_fr[y:y+h, x:x+w]

        # Let us resize the Image and store it as Region of Interest(roi)
        roi = cv2.resize(fc, (48, 48))

        # Let us make the Prediction of Emotion present in the Image
        pred_e = test_model.predict_emotion(roi[np.newaxis, :, :, np.newaxis])

        # Let us define text and its Font style that is to be written on Image representing the prediction
        sym = {"Happy": ":)", "Sad": ":}", "Surprise": "!!",
               "Angry": "?", "Disgust": "#", "Neutral": ".", "Fear": "~"}
        text = str(pred_e) + sym[str(pred_e)]
        font = cv2.FONT_HERSHEY_SIMPLEX

        # Inserting the Text on Image
        cv2.putText(img_p, text, (x, y), font, 0.7, (180, 105, 255), 2)

        # Finding the Coordinates and Radius of Circle
        xc = (x + x+w)/2
        yc = (y + y+h)/2
        radius = w/2  # (h/2) can also be used

        # Drawing the Circle on the Image
        cv2.circle(img_p, (int(xc), int(yc)), int(radius), (0, 255, 0), 2)

        # Saving the Predicted Image
        path = "static/" + "pred" + str(img)
        cv2.imwrite(path, img_p)

        # List of Emotions
        EMOTIONS = ["Angry", "Disgust",
                    "Fear", "Happy",
                    "Neutral", "Sad",
                    "Surprise"]

        # Finding the Probability of each Emotion
        preds = test_model.return_probabs(roi[np.newaxis, :, :, np.newaxis])

        # Converting into list
        data = preds.tolist()[0]

        # Initializing the Figure for Bar Graph
        fig = plt.figure(figsize=(8, 5))

        # Creating the bar plot
        plt.bar(EMOTIONS, data, color='green',
                width=0.4)

        # Labelling the axes and title
        plt.xlabel("Types of Emotions")
        plt.ylabel("Probability")
        plt.title("Face Emotion Recognition")

        # Saving the Bar Plot
        path = "static/" + "bar_plot" + str(img)
        plt.savefig(path)

    # returns a list containing the names of Original, Predicted, Bar Plot Images
    return ([img, "pred" + img, "bar_plot" + img])