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emotionclassification.py
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emotionclassification.py
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#!/usr/bin/env python3
# -*- coding = utf-8 -*-
import os
import sys
import json
import argparse
from statistics import mode
import cv2
import numpy as np
from keras.models import load_model
from data.load_data import get_fer2013_data
from models.model_factory import load_keras_model
from util.constant import fer2013_classes
from util.classifyimgops import apply_offsets
from util.classifyimgops import preprocess_input
from util.info import load_info
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--detector", default = "DNN",
help = "The detector used to detect faces: [mtcnn, dnn, cascade]")
ap.add_argument("-i", "--image", default = None,
help = "The images that you want to detect faces from.")
args = vars(ap.parse_args())
# Get Cascade Classifier and DNN Model.
face_detector, net = load_info()
# Choose Cascade vs DNN.
dnn = True
cascade = False
# Bounding.
image_window = 10
if dnn:
emotion_offsets = (45, 40)
elif cascade:
emotion_offsets = (30, 40)
else:
raise ValueError("You must choose either dnn or cascade.")
# Load Emotion Detection Model.
emotion_labels = fer2013_classes
emotion_classifier = load_keras_model('Model-27-0.6631', compile = False)
emotion_target_size = emotion_classifier.input_shape[1:3]
emotion_window = []
# If running from an IDE (not from command line), then enter images here.
savedir = "modded" # Directory to save changed images.
userimages = ["./test_imgs/amoghneutralnew.jpg"]
for image in userimages:
file, extension = os.path.splitext(image)
file = os.path.basename(file)
image = np.array(cv2.imread(image), dtype = 'uint8')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
global faces
if dnn:
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), swapRB=False, crop=False)
net.setInput(blob)
faces = net.forward()
if cascade:
faces = face_detector.detectMultiScale(gray_image, 1.3, 5)
if cascade:
for face_coordinates in faces:
print(face_coordinates)
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1: y2, x1: x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, False)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > image_window: emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)
except:
continue
if emotion_text == 'happy':
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion_text == 'angry':
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'sad':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'surprise':
color = emotion_probability * np.asarray((0, 255, 255))
else:
color = emotion_probability * np.asarray((0, 255, 0))
color = color.astype(int)
color = color.tolist()
x, y, w, h = face_coordinates
# cv2.rectangle(image, (x, y), (xe, ye), color, 3)
cv2.rectangle(image, (x, y), (x + w, y + h), color, 3)
cv2.putText(image, emotion_mode, (x + 0, y - 45), cv2.FONT_HERSHEY_SIMPLEX,
1, color, 3, cv2.LINE_AA)
# Print a background behind the text.
bg = np.full((image.shape), (0, 0, 0), dtype = np.uint8)
cv2.putText(bg, emotion_mode, (x + 0, y - 45), cv2.FONT_HERSHEY_SIMPLEX,
1, color, 3, cv2.LINE_AA)
x1, y1, w1, h1 = cv2.boundingRect(bg[:,:,2])
image[y1: y1 + h1, x1: x1 + w1] = bg[y1: y1 + h1, x1: x1 + w1]
if dnn:
(h, w) = image.shape[:2]
for k in range(0, faces.shape[2]):
c = faces[0, 0, k, 2]
if c < 0.5: continue
box = faces[0, 0, k, 3:7] * np.array([w, h, w, h])
(x, y, xe, ye) = box.astype("int")
face_coordinates = (x, y, xe, ye)
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1: y2, x1: x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = cv2.flip(gray_face, 1)
# gray_face = preprocess_input(gray_face, False)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > image_window: emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)
except:
continue
if emotion_text == 'happy':
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion_text == 'angry':
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'sad':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'surprise':
color = emotion_probability * np.asarray((0, 255, 255))
else:
color = emotion_probability * np.asarray((0, 255, 0))
color = color.astype(int)
color = color.tolist()
x, y, w, h = face_coordinates
cv2.rectangle(image, (x, y), (xe, ye), color, 3)
# cv2.rectangle(image, (x, y), (x + w, y + h), color, 3)
cv2.putText(image, emotion_mode, (x + 0, y - 80), cv2.FONT_HERSHEY_SIMPLEX,
3, color, 3, cv2.LINE_AA)
# Print a background behind the text.
# bg = np.full((image.shape), (0, 0, 0), dtype = np.uint8)
# cv2.putText(bg, emotion_mode, (x + 0, y - 45), cv2.FONT_HERSHEY_SIMPLEX,
# 1, color, 3, cv2.LINE_AA)
# x1, y1, w1, h1 = cv2.boundingRect(bg[:,:,2])
# print(x1, y1, w1, h1)
# image[y1: y1 + h1, x1: x1 + w1] = bg[y1: y1 + h1, x1: x1 + w1]
cv2.imwrite(os.path.join(savedir or "", f"{file}-detect{extension}"), image)