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import gradio as gr
import os
import cv2
import numpy as np
import imutils
from keras.preprocessing.image import img_to_array
from keras.models import load_model
# Load the pre-trained models and define parameters
detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
emotion_model_path = 'model4_0.83/model4_entire_model.h5'
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']
# face_detector_mtcnn = MTCNN()
classifier = load_model(emotion_model_path)
# def predict_emotion(frame):
# frame = imutils.resize(frame, width=300)
# gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
# minNeighbors=5, minSize=(30, 30),
# flags=cv2.CASCADE_SCALE_IMAGE)
# frame_clone = frame.copy()
# if len(faces) > 0:
# faces = sorted(faces, reverse=True,
# key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
# (fX, fY, fW, fH) = faces
# # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
# # the ROI for classification via the CNN
# roi = gray[fY:fY + fH, fX:fX + fW]
# roi = cv2.resize(roi, (48, 48))
# roi = roi.astype("float") / 255.0
# roi = img_to_array(roi)
# roi = np.expand_dims(roi, axis=0)
# preds = emotion_classifier.predict(roi)[0]
# label = EMOTIONS[preds.argmax()]
# # Overlay a box over the detected face
# cv2.putText(frame_clone, label, (fX, fY + 100),
# cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
# cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
# (238, 164, 64), 2)
# else:
# label = "Can't find your face"
# return frame_clone
def predict_emotion(frame):
frame = imutils.resize(frame, width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=5, minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
frame_clone = frame.copy()
for (fX, fY, fW, fH) in faces:
# Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
# the ROI for classification via the CNN
roi = gray[fY:fY + fH, fX:fX + fW]
roi = cv2.resize(roi, (48, 48))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
preds = emotion_classifier.predict(roi)[0]
label = EMOTIONS[preds.argmax()]
# Overlay a box over the detected face
cv2.putText(frame_clone, label, (fX, fY - 10),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (238, 164, 64), 1)
cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
(238, 164, 64), 2)
return frame
demo = gr.Interface(
fn = predict_emotion,
inputs = gr.Image(type="numpy"),
outputs = gr.Image(),
# gr.components.Image(label="Predicted Emotion"),
# gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
#flagging_options=["blurry", "incorrect", "other"],
examples = [
os.path.join(os.path.dirname(__file__), "images/chandler.jpeg"),
os.path.join(os.path.dirname(__file__), "images/janice.jpeg"),
os.path.join(os.path.dirname(__file__), "images/joey.jpeg"),
os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"),
os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"),
os.path.join(os.path.dirname(__file__), "images/ross.jpeg"),
os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
],
title = "Whatchu feeling?",
theme = "shivi/calm_seafoam"
)
if __name__ == "__main__":
demo.launch() |