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""" |
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Created on Sun Dec 25 08:38:00 2022 |
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@author: ROSHAN |
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""" |
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import tensorflow as tf |
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import gradio as gr |
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import numpy as np |
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import cv2 |
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from PIL import Image as im |
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from matplotlib import pyplot as plt |
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cls=['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'] |
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model = tf.keras.models.load_model("56fer.h5") |
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') |
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def show(img): |
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img=img[:, :, ::-1].copy() |
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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faces = face_cascade.detectMultiScale(gray, 1.5, 1) |
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r=[] |
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x=faces[0][0] |
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y=faces[0][1] |
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w=faces[0][2] |
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h=faces[0][3] |
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cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2) |
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r.append(img) |
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sharp_kernel = np.array([[0, -1, 0], |
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[-1, 5, -1], |
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[0, -1, 0]]) |
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sharp_img = cv2.filter2D(src=gray, ddepth=-1, kernel=sharp_kernel) |
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crop_img = sharp_img[y:y+h, x:x+w] |
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npa=np.array(crop_img)/255.0 |
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predictions = model.predict(np.resize(npa,(48,48)).reshape(-1,48,48,1)) |
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score =predictions[0] |
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score=tf.nn.softmax(predictions[0]) |
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plt.figure() |
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confidences = {cls[i]: float(score[i]) for i in range(len(cls))} |
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return confidences |
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demo = gr.Interface( |
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fn=show, |
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inputs="image", |
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outputs=gr.outputs.Label(num_top_classes=7), |
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) |
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demo.launch() |
