/
videoclassification.py
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/
videoclassification.py
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#!/usr/bin/env python3
# -*- coding = utf-8 -*-
import os
import sys
import json
import statistics
import cv2
from keras.models import load_model
import numpy as np
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
# Window Position Coordinates (MacBook Pro 13-inch)
CENTER_X = 100
CENTER_Y = 80
CENTER_POS = (CENTER_X, CENTER_Y)
# Get DNN Model
face_detector, net = load_info()
# Choose Model (only one)
dnn = True
cascade = False
# Bounding
frame_window = 10
if dnn:
emotion_offsets = (55, 45)
elif cascade:
emotion_offsets = (20, 40)
else:
raise ValueError("You must select one of dnn or cascade.")
# Load Emotion Model.
emotion_model_path = os.path.join(os.path.dirname(__file__), 'data/savedmodels/Model-49-0.6572.hdf5')
emotion_labels = fer2013_classes
emotion_classifier = load_keras_model('Model-27-0.6631', compile = False)
emotion_target_size = emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
emotion_window = []
# Bring the video screen to the front (MACOS ONLY).
if sys.platform == "darwin": os.system("""osascript -e 'tell app "Finder" to set frontmost of process "Python" to be true'""")
def showPositionedWindow(window_name, img_name, coords):
cv2.namedWindow(window_name)
cv2.moveWindow(window_name, coords[0], coords[1])
cv2.imshow(window_name, img_name)
vr = cv2.VideoCapture(0)
global faces
while True:
_, frame = vr.read()
gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if dnn:
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (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:
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
# Preprocess & Classify Instantaneous Emotion
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
# Set up a range of emotions from which to display from.
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) > frame_window: emotion_window.pop(0)
try: emotion_mode = statistics.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(frame, (x1, y1), (x2, y2), color, 3)
cv2.putText(frame, emotion_mode, (x + 0, y - 45), cv2.FONT_HERSHEY_SIMPLEX,
1, color, 3, cv2.LINE_AA)
if dnn:
(h, w) = frame.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 - x, ye - y)
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
# Preprocess & Classify Instantaneous Emotion
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
# Set up a range of emotions from which to display from.
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) > frame_window: emotion_window.pop(0)
try: emotion_mode = statistics.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).tolist()
# For Testing:
# cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3)
cv2.rectangle(frame, (x, y), (xe, ye), color, 3)
cv2.putText(frame, emotion_mode, (x + 0, y - 45), cv2.FONT_HERSHEY_SIMPLEX,
1, color, 3, cv2.LINE_AA)
showPositionedWindow('frame', frame, CENTER_POS)
if cv2.waitKey(1) and 0xFF == ord('z'):
break
vr.release()
cv2.destroyAllWindows()