import streamlit as st from PIL import Image import face_recognition import os import uuid import streamlit as st from PIL import Image import face_recognition st.header("Face Detection") st.write( "Now it's time to collect the pictures we need to create our known-faces data base for our face recognition model. " "But remember, we should always ask for permission before taking someone's picture. We can use a smartphone or a digital camera to capture pictures, and it's important to take pictures of different people. This will help our application to have a good known-faces database!" ) img_dir = os.path.join(".sessions", "known_faces") os.makedirs(img_dir, exist_ok=True) picture = st.file_uploader("Upload a candidate image",type=['jpg','png','jpeg'],accept_multiple_files=False) if picture: image = face_recognition.load_image_file(picture) st.image(image) # Find all the faces in the image using the default HOG-based model. # This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated. # See also: find_faces_in_picture_cnn.py face_locations = face_recognition.face_locations(image) st.write("Algorithm found {} face(s) in this photograph.".format(len(face_locations))) cols = st.columns(len(face_locations)) for i in range(len(face_locations)): col = cols[i] face = face_locations[i] # display faces with col: st.header("Face {}".format(i)) # Print the location of each face in this image top, right, bottom, left = face # You can access the actual face itself like this: face_image = image[top:bottom, left:right] pil_image = Image.fromarray(face_image) st.image(pil_image) face_name = st.text_input('Specify name', "This is a placeholder", key="text_"+str(i)) if st.button("Save", key="button_"+str(i)): img_name = str(uuid.uuid4()) + f"{face_name}" + ".jpg" img_path = os.path.join(img_dir, img_name) with open(img_path, "wb") as f: f.write(face_image) st.success("Face added successfully!") else: st.write("Please upload an image to proceed.")