Spaces:
Sleeping
Sleeping
File size: 4,212 Bytes
5971329 88142f2 5971329 b125ffc 5971329 b125ffc 5971329 136ac77 5971329 b125ffc 5971329 136ac77 132b5af 320484d cdadc4f 2b8f874 1753039 5971329 320484d cdadc4f 136ac77 cdadc4f 136ac77 cdadc4f 61e9d71 6931130 61e9d71 cdadc4f 136ac77 320484d b207167 136ac77 2b8f874 136ac77 c430995 b03b518 136ac77 99b7bd9 136ac77 fdc7694 136ac77 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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_clone, (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() |