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Update app.py
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app.py
CHANGED
@@ -18,58 +18,6 @@ emotion_classifier = load_model(emotion_model_path, compile=False)
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EMOTIONS = ['neutral','happiness','surprise','sadness','anger','disgust','fear','contempt','unknown']
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# def predict(frame):
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# frame = imutils.resize(frame, width=300)
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# gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
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# faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
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# minNeighbors=5, minSize=(30, 30),
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# flags=cv2.CASCADE_SCALE_IMAGE)
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# frameClone = frame.copy()
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# if len(faces) > 0:
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# faces = sorted(faces, reverse=True,
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# key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
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# (fX, fY, fW, fH) = faces
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# # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
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# # the ROI for classification via the CNN
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# roi = gray[fY:fY + fH, fX:fX + fW]
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# roi = cv2.resize(roi, (48, 48))
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# roi = roi.astype("float") / 255.0
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# roi = img_to_array(roi)
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# roi = np.expand_dims(roi, axis=0)
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# preds = emotion_classifier.predict(roi)[0]
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# label = EMOTIONS[preds.argmax()]
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# else:
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# return frameClone, "Can't find your face"
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# probs = {}
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# cv2.putText(frameClone, label, (fX, fY - 10),
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# cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
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# cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
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# (238, 164, 64), 2)
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# for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
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# probs[emotion] = float(prob)
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# return frameClone, probs
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# inp = gr.components.Image(sources="webcam", label="Your face")
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# out = [
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# gr.components.Image(label="Predicted Emotion"),
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# gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
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# ]
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# title = "Facial Emotion Recognition"
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# description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \
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# " you are: happy, sad, angry, disgusted, scared, surprised, or neutral."
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# thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png"
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# # gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail,
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# # description=description).launch(inbrowser=True)
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# gr.Interface(fn=predict, inputs=inp, outputs=out, title=title, thumbnail=thumbnail).launch()
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######################################################################################################################################################
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def predict(frame):
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frame = imutils.resize(frame, width=300)
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@@ -107,17 +55,16 @@ def predict(frame):
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return frameClone, probs
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inp = gr.components.
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out = [
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gr.components.
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gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
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title = "Facial Emotion Recognition"
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description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \
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" you are: happy, sad, angry, disgusted, scared, surprised, or neutral."
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thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png"
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gr.Interface(fn=predict, inputs=inp, outputs=out, title=title, thumbnail=thumbnail).launch()
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EMOTIONS = ['neutral','happiness','surprise','sadness','anger','disgust','fear','contempt','unknown']
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def predict(frame):
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frame = imutils.resize(frame, width=300)
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return frameClone, probs
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inp = gr.components.Image(sources="webcam", label="Your face")
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out = [
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gr.components.Image(label="Predicted Emotion"),
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gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
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]
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title = "Facial Emotion Recognition"
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description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \
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" you are: happy, sad, angry, disgusted, scared, surprised, or neutral."
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thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png"
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# gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail,
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# description=description).launch(inbrowser=True)
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gr.Interface(fn=predict, inputs=inp, outputs=out, title=title, thumbnail=thumbnail).launch()
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