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app_streamlit.py

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+import cv2
+import streamlit as st
+import numpy as np
+from keras.models import load_model
+# import tempfile
+
+# Load the pre-trained model
+model = load_model('keras_model.h5')
+
+# Load the class labels
+with open('labels.txt', 'r') as f:
+    class_names = [line.strip() for line in f.readlines()]
+
+# Load the Haar cascade classifier for face detection
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+font = cv2.FONT_HERSHEY_SIMPLEX
+
+# Use this line to capture video from the webcam
+cap = cv2.VideoCapture(0)
+
+# Set the title for the Streamlit app
+st.title("Video Capture with OpenCV")
+
+frame_placeholder = st.empty()
+
+# Add a "Stop" button and store its state in a variable
+stop_button_pressed = st.button("Stop")
+
+while cap.isOpened() and not stop_button_pressed:
+    ret, frame = cap.read()
+    # Detect faces in the frame
+    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.3, minNeighbors=5)
+    if not ret:
+        st.write("The video capture has ended.")
+        break
+
+    # You can process the frame here if needed
+    # e.g., apply filters, transformations, or object detection
+    for (x, y, w, h) in faces:
+            # Extract the face region
+            face_img = frame[y:y+h, x:x+w]
+
+            # Preprocess the face image
+            face_img = cv2.resize(face_img, (224, 224))
+            face_img = np.expand_dims(face_img, axis=0)
+            face_img = face_img / 255.0
+
+            # Predict the class probabilities
+            pred_probs = model.predict(face_img)[0]
+            class_idx = np.argmax(pred_probs)
+            class_prob = pred_probs[class_idx]
+
+            # Get the class name and display it on the image
+            class_name = class_names[class_idx]
+            if class_prob*100 < 70:
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
+                text = '{}: {:.2f}%'.format('Unknown', class_prob * 100)
+                cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
+            else:
+                cv2.putText(frame, class_name, (x, y - 10), font, 1, (0, 255, 0), 2)
+                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                text = '{}: {:.2f}%'.format(class_name, class_prob * 100)
+                cv2.putText(frame, text, (x, y + h + 30), font, 0.5, (0, 255, 0), 1)
+    # Convert the frame from BGR to RGB format
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    # Display the frame using Streamlit's st.image
+    frame_placeholder.image(frame, channels="RGB")
+
+    # Break the loop if the 'q' key is pressed or the user clicks the "Stop" button
+    if cv2.waitKey(1) & 0xFF == ord("q") or stop_button_pressed:
+        break
+
+cap.release()
+cv2.destroyAllWindows()

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f8e70aa553db5a361cb8fa0adea3f593741674374590fed2b250cd344782493
+size 2453432

labels.txt

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+0 Aldin
+1 Rowan