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manual_poc.py
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manual_poc.py
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# Proof-of-concept
import cv2
import sys
import os
from constants import *
from emotion_recognition import EmotionRecognition
import numpy as np
def format_image(image):
if len(image.shape) > 2 and image.shape[2] == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
image = cv2.imdecode(image, cv2.CV_LOAD_IMAGE_GRAYSCALE)
faces = cv2.CascadeClassifier(CASC_PATH).detectMultiScale(
image,
scaleFactor=1.3,
minNeighbors=5
)
# None is we don't found an image
if not len(faces) > 0:
return None
max_area_face = faces[0]
for face in faces:
if face[2] * face[3] > max_area_face[2] * max_area_face[3]:
max_area_face = face
# Chop image to face
face = max_area_face
image = image[face[1]:(face[1] + face[2]), face[0]:(face[0] + face[3])]
# Resize image to network size
try:
image = cv2.resize(image, (SIZE_FACE, SIZE_FACE),
interpolation=cv2.INTER_CUBIC) / 255.
while True:
cv2.imshow("frame", image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except Exception:
print("[+] Problem during resize")
return None
# cv2.imshow("Lol", image)
# cv2.waitKey(0)
return image
# Load Model
network = EmotionRecognition()
network.build_network()
files = []
for f in os.listdir("./"):
ext = os.path.splitext(f)[1]
if ext.lower() in [".jpg"]:
files.append(f)
for f in files:
frame = cv2.imread(f)
# Predict result with network
result = network.predict(format_image(frame))
if result is not None:
for index, emotion in enumerate(EMOTIONS):
print(emotion, ': ', result[0][index])
print("Emotion: of ", f, "-", EMOTIONS[np.argmax(result[0])])