# Facial Recognition with Emotion / Sentiment Detector

# This is a custom, hard-coded version of darknet with 
# YOLOv3 implementation for openimages database. This 
# was written to test viability of implementing YOLO 
# for face detection followed by emotion / sentiment
# analysis.
#
# Configuration, weights and data are hardcoded.
# This version takes any images, detects faces,
# and then runs emotion / sentiment analysis
#
# Author    : Saikiran Tharimena
# Co-Authors: Kjetil Marinius Sjulsen, Juan Carlos Calvet Lopez
# Project   : Emotion / Sentiment Detection from news images
# Date      : 12 September 2022
# Version   : v0.1
#
# (C) Schibsted ASA

# Libraries
import torch
import pickle
from utils import *
import gradio as gr
from numpy import array
from torch.autograd import Variable
from torch.cuda import is_available as check_cuda
from PIL.ImageOps import grayscale
from fastai.vision.all import PILImage, load_learner

################## DARKNET ##################
# Parameters
batch_size = 1
confidence = 0.25
nms_thresh = 0.30
run_cuda = False

# CFG Files
clsnames= 'cfg/openimages.names'

# Load classes
classes = load_classes(clsnames)
num_classes = len(classes)

# Set up the neural network
print('Load Network')
with open('models/darknet.pkl', 'rb') as f:
    model = pickle.load(f)

print('Successfully loaded Network')

# Check CUDA
if run_cuda:
    CUDA = check_cuda()
else:
    CUDA = False

# Input dimension
inp_dim = int(model.net_info["height"])

# put the model on GPU
if CUDA:
    model.cuda()

# Set the model in evaluation mode
model.eval()

def get_detections(x):
    c1 = [int(y) for y in x[1:3]]
    c2 = [int(y) for y in x[3:5]]
    
    det_class = int(x[-1])
    label = "{0}".format(classes[det_class])
    
    return (label, tuple(c1 + c2))

# face detector
def detector(image):
    # Just lazy to update this
    imlist = [image]
    loaded_ims = [image]

    im_batches = list(map(prep_image, loaded_ims, [inp_dim for x in range(len(imlist))]))
    im_dim_list = [(x.shape[1], x.shape[0]) for x in loaded_ims]
    im_dim_list = torch.FloatTensor(im_dim_list).repeat(1,2)

    leftover = 0
    if (len(im_dim_list) % batch_size):
        leftover = 1

    if batch_size != 1:
        num_batches = len(imlist) // batch_size + leftover            
        im_batches = [torch.cat((im_batches[i*batch_size : min((i +  1)*batch_size,
                            len(im_batches))]))  for i in range(num_batches)]  

    write = 0
    if CUDA:
        im_dim_list = im_dim_list.cuda()
    
    for i, batch in enumerate(im_batches):
        # load the image 
        
        if CUDA:
            batch = batch.cuda()
        with torch.no_grad():
            prediction = model(Variable(batch), CUDA)

        prediction = write_results(prediction, confidence, num_classes, nms_conf = nms_thresh)

        if type(prediction) == int:

            for im_num, image in enumerate(imlist[i*batch_size: min((i +  1)*batch_size, len(imlist))]):
                im_id = i*batch_size + im_num
                
            continue

        prediction[:,0] += i*batch_size    # transform the atribute from index in batch to index in imlist 

        if not write: # If we have't initialised output
            output = prediction  
            write = 1
        else:
            output = torch.cat((output, prediction))

        for im_num, image in enumerate(imlist[i*batch_size: min((i +  1)*batch_size, len(imlist))]):
            im_id = i * batch_size + im_num
            objs = [classes[int(x[-1])] for x in output if int(x[0]) == im_id]

        if CUDA:
            torch.cuda.synchronize()
       
    try:
        output
    except NameError:
        return None

    im_dim_list = torch.index_select(im_dim_list, 0, output[:,0].long())

    scaling_factor = torch.min(608/im_dim_list,1)[0].view(-1,1)

    output[:, [1,3]] -= (inp_dim - scaling_factor*im_dim_list[:,0].view(-1,1))/2
    output[:, [2,4]] -= (inp_dim - scaling_factor*im_dim_list[:,1].view(-1,1))/2

    output[:, 1:5] /= scaling_factor

    for i in range(output.shape[0]):
        output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim_list[i,0])
        output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim_list[i,1])
    
    detections = list(map(get_detections, output))

    if CUDA:
        torch.cuda.empty_cache()
    
    return loaded_ims[0], detections
#############################################


# Emotion
learn_emotion = load_learner('models/emotions_vgg19.pkl')
learn_emotion_labels = learn_emotion.dls.vocab

# Sentiment
learn_sentiment = load_learner('models/sentiment_vgg19.pkl')
learn_sentiment_labels = learn_sentiment.dls.vocab

def crop_images(img, bbox):
    "Here image should be an image object from PILImage.create"
    
    # Coordinates of face in cv2 format
    xmin, ymin, xmax, ymax = bbox[1]

    # resize and crop face
    return img.crop((xmin, ymin, xmax, ymax))


def detect_person_face(img, detections):
    '''This function is called from within detect face.
    If only a person is detected, then this will crop
    image and then try to detect face again.'''

    faces = []

    # Loop through people
    for detection in detections:
        
        # Get cropped image of person
        temp = crop_images(img, detection)

        # run detector again
        _, detect = detector(array(temp)[...,:3])
        
        # check for human faces
        human_face = [idx for idx, val in enumerate(detect) if val[0] == 'Human face']

        if len(human_face) == 0:
            continue
        
        # Force it to take only 1 face per person
        # crop face and append to list
        faces.append(crop_images(temp, detect[human_face[0]]))

    return faces


def detect_face(img):
    
    _, detections = detector(array(img)[...,:3])

    # check for human faces
    human_face = [idx for idx, val in enumerate(detections) if val[0] == 'Human face']

    if len(human_face) == 0:
        human_face = [idx for idx, val in enumerate(detections) if val[0] == 'Person']
    
        if len(human_face) == 0:
            return None
        else:
            # Only get human face detections
            faces = detect_person_face(img, [detections[idx] for idx in human_face])
    
    else:
        # Only get human face detections
        faces = []

        for idx in human_face:
            faces.append(crop_images(img, detections[idx]))
    
    return faces


# Predict
def predict(img):

    img = PILImage.create(img)

    # Detect faces
    faces = detect_face(img)

    output = []

    if len(faces) == 0:

        img = img.resize(48, 48)

        pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(array(grayscale(img)))
    
        pred_sentiment, pred_sentiment_idx, probs_sentiment = learn_sentiment.predict(array(grayscale(img)))
    
        emotions = {learn_emotion_labels[i]: float(probs_emotion[i]) for i in range(len(learn_emotion_labels))}
        sentiments = {learn_sentiment_labels[i]: float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))}

        output = [img, emotions, sentiments, None, None, None, None, None, None]
    
    else: # Max 3 for now
        for face in faces[:3]:
            
            img = face.resize((48, 48))

            pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(array(grayscale(img)))
    
            pred_sentiment, pred_sentiment_idx, probs_sentiment = learn_sentiment.predict(array(grayscale(img)))
        
            emotions = {learn_emotion_labels[i]: float(probs_emotion[i]) for i in range(len(learn_emotion_labels))}
            sentiments = {learn_sentiment_labels[i]: float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))}

            output.append(img)
            output.append(emotions)
            output.append(sentiments)
        
        temp = output[-3:]
        while len(output) < 9:
            output = output + temp

    return output

# Gradio
title = 'Face Recognition with Emotion and Sentiment Detector'

description = gr.Markdown(
                """Ever wondered what a person might be feeling looking at their picture? 
                 Well, now you can! Try this fun app. Just upload a facial image in JPG or
                 PNG format. Voila! you can now see what they might have felt when the picture
                 was taken.

                 This is an updated version of Facial Expression Classifier: 
                 https://huggingface.co/spaces/schibsted/facial_expression_classifier
                 """).value

article = gr.Markdown(
             """**DISCLAIMER:** This model does not reveal the actual emotional state of a person. Use and 
             interpret results at your own risk! It was built as a demo for AI course. Samples images
             were downloaded from VG & AftenPosten news webpages. Copyrights belong to respective
             brands. All rights reserved.
             
             **PREMISE:** The idea is to determine an overall sentiment of a news site on a daily basis
             based on the pictures. We are restricting pictures to only include close-up facial
             images.
             
             **DATA:** FER2013 dataset consists of 48x48 pixel grayscale images of faces. There are 28,709 
             images in the training set and 3,589 images in the test set. However, for this demo all 
             pictures were combined into a single dataset and 80:20 split was used for training. Images
             are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral.
             In addition to these 7 classes, images were re-classified into 3 sentiment categories based
             on emotions:
             
             Positive (Happy, Surprise)
             
             Negative (Angry, Disgust, Fear, Sad)
             
             Neutral (Neutral)
             
             FER2013 (preliminary version) dataset can be downloaded at: 
             https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
             
             **EMOTION / SENTIMENT MODEL:** VGG19 was used as the base model and trained on FER2013 dataset. Model was trained
             using PyTorch and FastAI. Two models were trained, one for detecting emotion and the other
             for detecting sentiment. Although, this could have been done with just one model, here two
             models were trained for the demo.
             
             **FACE DETECTOR:** Darknet with YOLOv3 architecture was used for face detection. Reach out to me for full details.
             In short, any image is first sent through darknet. If face is detected, then it is passed through emotion/sentiment 
             model for each face in the picture. If a person is detected rather than a face, the image is cropped and run through
             face detector again. If a face is detected, then it is passed through emotion/sentiment model. In case face is not
             detected in an image, then the entire image is evaluated to generate some score. This is done because, I couldn't
             figure out how to pipe None/blank output to Gradio.Interface(). There maybe option through Gradio.Blocks() but was
             too lazy to go through that at this stage. In addition, the output is restricted to only 3 faces in a picture. 
             """).value

enable_queue=True

examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg']

gr.Interface(fn = predict, 
             inputs = gr.Image(), 
             outputs = [gr.Image(shape=(12, 12), label='Person 1'), 
                        gr.Label(label='Emotion - Person 1'), 
                        gr.Label(label='Sentiment - Person 1'),
                        gr.Image(shape=(12, 12), label='Person 2'), 
                        gr.Label(label='Emotion - Person 2'), 
                        gr.Label(label='Sentiment - Person 2'),
                        gr.Image(shape=(12, 12), label='Person 3'), 
                        gr.Label(label='Emotion - Person 3'), 
                        gr.Label(label='Sentiment - Person 3'),], #gr.Label(),
             title = title,
             examples = examples,
             description = description,
             article=article,
             allow_flagging='never').launch(enable_queue=enable_queue)