FaceTune/emotionrecognisewithtime.py at main · nirutthyu/FaceTune · GitHub

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# -*- coding: utf-8 -*-
"""EmotionRecogniseWithTime.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1qg14s0qdUbPVsFzVoLIuH-4qb8p2iLo3
"""

import tensorflow as tf
import keras
import os
import cv2
import random
from tensorflow import keras
from keras import layers
import numpy as np
import matplotlib.pyplot as plt

path = "haarcascade_frontalface_default.xml"
font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

rectangle_bgr = (255, 255, 255)

img = np.zeros((500, 500))

text = "Some text in a box!"

(text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]

text_offset_x = 10
text_offset_y = img.shape[0] - 25

box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height - 2))
cv2.rectangle(img, box_coords[0], box_coords [1], rectangle_bgr, cv2. FILLED)
cv2. putText(img, text, (text_offset_x, text_offset_y), font, fontScale=font_scale, color=(0, 0, 0), thickness=1)

import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import random
import webbrowser
import time

def camera_module():
    hap = sad = sur = dis = ang = neut = 0
    cap = cv2.VideoCapture(0)
    faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    flag=0
    emote="neutral"
    while True:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = faceCascade.detectMultiScale(gray, 1.1, 4)

        face_roi = None

        for x, y, w, h in faces:
            roi_gray = gray[y:y+h, x:x+w]
            roi_color = frame[y:y+h, x:x+w]
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
            facess = faceCascade.detectMultiScale(roi_gray)

            for (ex, ey, ew, eh) in facess:
                face_roi = roi_color[ey: ey+eh, ex:ex + ew]

        if face_roi is not None:
            final_image = cv2.resize(face_roi, (224, 224))
            final_image = np.expand_dims(final_image, axis=0)
            final_image = final_image / 255.0

            new_model = tf.keras.models.load_model('final_model_ishu1.h5')

            prediction = new_model.predict(final_image)

            font = cv2.FONT_HERSHEY_SIMPLEX
            font_scale = 1.5

            if np.argmax(prediction) == 3:
                status = "happy"
                hap += 1
            elif np.argmax(prediction) == 5:
                status = "sad"
                sad += 1
            elif np.argmax(prediction) == 6:
                status = "surprise"
                sur += 1
            elif np.argmax(prediction) == 1:
                status = "disgust"
                dis += 1
            elif np.argmax(prediction) == 0:
                status = "angry"
                ang += 1
            else:
                status = "neutral"
                neut += 1
            print(status)

            if(hap>5 or sad>5 or sur>5 or dis>5 or ang>5 or neut>5):
                # flag=1
                emote=status
                cv2.putText(frame, emote, (100, 150), font, 3, (0, 0, 255), 2, cv2.LINE_4)
                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)

                if(emote=="angry"):
                    lz_uri = 'spotify:artist:6AiX12wXdXFoGJ2vk8zBjy' #yuvan

                    spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id='5a58176dab9b49c9a1467f0e4a92763f',client_secret='666f8bedbdfe4c7f8400dda9b78ed039'))
                    results = spotify.artist_top_tracks(lz_uri)

                    aud=[]
                    for track in results['tracks'][:2]:
                        aud.append(track['preview_url'])

                    random.shuffle(aud)

                    for i in aud:
                        webbrowser.open(i)
                        print(i)
                        time.sleep(30)
                elif(emote=="happy" or emote=="surprise"):
                    lz_uri = 'spotify:artist:3m49WVMU4zCkaVEKb8kFW7' #illaiyaraja

                    spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id='5a58176dab9b49c9a1467f0e4a92763f',client_secret='666f8bedbdfe4c7f8400dda9b78ed039'))
                    results = spotify.artist_top_tracks(lz_uri)

                    aud=[]
                    for track in results['tracks'][:2]:
                        aud.append(track['preview_url'])

                    random.shuffle(aud)

                    for i in aud:
                        webbrowser.open(i)
                        print(i)
                        time.sleep(30)
                elif(emote=="sad" or emote=="neutral"):

                        lz_uri = 'spotify:artist:6HtEIJH43LAZGQx6iNCqhg' #ganabala

                        spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id='5a58176dab9b49c9a1467f0e4a92763f',client_secret='666f8bedbdfe4c7f8400dda9b78ed039'))
                        results = spotify.artist_top_tracks(lz_uri)
                        aud=[]
                        for track in results['tracks'][:2]:
                            aud.append(track['preview_url'])
                            random.shuffle(aud)
                        for i in aud:
                            webbrowser.open(i)
                            print(i)
                            time.sleep(30)
                hap = sad = sur = dis = ang = neut = 0

        cv2.imshow("Face emotion", frame)
        k = cv2.waitKey(10)
        if k == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

camera_module()