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from deepface import DeepFace
import numpy as np
from PIL import Image, ExifTags
# used to raise custom exceptions
class ImageProcessingError(Exception):
pass
# used to ensure upright photo orientation for face detection
def check_image_rotation(image_path):
try:
image = Image.open(image_path)
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == 'Orientation':
break
exif = image._getexif()
if exif is not None:
if orientation in exif:
if exif[orientation] == 3:
image = image.rotate(180, expand=True)
elif exif[orientation] == 6:
image = image.rotate(270, expand=True)
elif exif[orientation] == 8:
image = image.rotate(90, expand=True)
return image
except (AttributeError, KeyError, IndexError):
# If the orientation tag is not found or any other error occurs, return the original image
return Image.open(image_path)
# used to process photo through deepface emotion model
def process_photo(file_name):
backends = ['opencv', 'mtcnn', 'retinaface', 'mediapipe', 'ssd']
attempt = 0
image = check_image_rotation(file_name)
image_data = np.array(image)
while attempt < len(backends):
try:
predictions = DeepFace.analyze(image_data, actions=['emotion'], detector_backend=backends[attempt])
if len(predictions) > 1:
faces = [(face, face['region']['w'] * face['region']['h']) for face in predictions]
new_predictions = sorted(faces, key=lambda x: x[1], reverse=True)[0][0]
emotion_dict = new_predictions['emotion']
return emotion_dict
return predictions['emotion']
except Exception as e:
if attempt == len(backends) - 1:
error_message = f"Failed to analyze image after attempting all detector backends available. Please upload a new image."
raise ImageProcessingError(error_message)
else:
# log the error message for each failed backend here:
print(f"Retrying with backend `{backends[attempt+1]}` due to error: {str(e)}")
attempt += 1
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