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Update app.py
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app.py
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@@ -1,9 +1,15 @@
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import streamlit as st
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import cv2
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import numpy as np
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# Load the pre-trained object detection model
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model = cv2.CascadeClassifier(
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# Function to perform object recognition
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def detect_objects(image):
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@@ -14,15 +20,23 @@ def detect_objects(image):
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return image
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# Streamlit app
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st.title('
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uploaded_file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
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if uploaded_file is not None:
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# Read the uploaded image
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image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
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-
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import streamlit as st
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import cv2
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import numpy as np
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import urllib.request
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# Download the Haar cascade XML file
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cascade_file_url = "https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml"
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cascade_file_path = "haarcascade_frontalface_default.xml"
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urllib.request.urlretrieve(cascade_file_url, cascade_file_path)
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# Load the pre-trained object detection model
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model = cv2.CascadeClassifier(cascade_file_path)
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# Function to perform object recognition
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def detect_objects(image):
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return image
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# Streamlit app
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st.title('Face Detection')
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uploaded_file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
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if uploaded_file is not None:
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# Read the uploaded image
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image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
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if image is None:
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st.write('Error: Unable to read the image. Please make sure the uploaded file is a valid image.')
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else:
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# Perform object recognition
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result = detect_objects(image)
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if result is None:
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st.write('Error: Face detection failed.')
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else:
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# Display the result
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st.image(result, channels='BGR')
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