Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| # from transformers import pipeline | |
| from PIL import Image | |
| import face_recognition | |
| import cv2 | |
| import numpy as np | |
| import requests | |
| import os | |
| st.title("AIMLJan24 - Face Recognition") | |
| # create list of encoding of all images in photos folder | |
| # Load images for face recognition | |
| Images = [] # List to store Images | |
| classnames = [] # List to store classnames | |
| directory = "photos" | |
| myList = os.listdir(directory) | |
| st.write("Photographs found in folder : ") | |
| for cls in myList: | |
| if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]: | |
| img_path = os.path.join(directory, cls) | |
| curImg = cv2.imread(img_path) | |
| Images.append(curImg) | |
| st.write(os.path.splitext(cls)[0]) | |
| classnames.append(os.path.splitext(cls)[0]) | |
| # Load images for face recognition | |
| encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images] | |
| # camera to take photo of user in question | |
| file_name = st.camera_input("Take a picture") #st.file_uploader("Upload image ") | |
| if file_name is not None: | |
| col1, col2 = st.columns(2) | |
| test_image = Image.open(file_name) | |
| image = np.asarray(test_image) | |
| imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25) | |
| imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB) | |
| facesCurFrame = face_recognition.face_locations(imgS) | |
| encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame) | |
| name = "Unknown" # Default name for unknown faces | |
| match_found = False # Flag to track if a match is found | |
| # Checking if faces are detected | |
| if len(encodesCurFrame) > 0: | |
| for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame): | |
| # Assuming that encodeListknown is defined and populated in your code | |
| matches = face_recognition.compare_faces(encodeListknown, encodeFace) | |
| faceDis = face_recognition.face_distance(encodeListknown, encodeFace) | |
| matchIndex = np.argmin(faceDis) | |
| if matches[matchIndex]: | |
| name = classnames[matchIndex].upper() | |
| match_found = True # Set the flag to True | |
| y1, x2, y2, x1 = faceLoc | |
| y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4) | |
| cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED) | |
| cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2) | |
| st.image(image, use_column_width=True, output_format="PNG") | |
| else: | |
| st.warning("No faces detected in the image. Face recognition failed.") | |
| # image = Image.open(file_name) | |
| # col1.image(image, use_column_width=True) | |
| # pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
| # st.title("AIMLJan24 First App on Hugging face - Hot Dog? Or Not?") | |
| # file_name = st.file_uploader("Upload the test image to find is this hot dog ! ") | |
| # if file_name is not None: | |
| # col1, col2 = st.columns(2) | |
| # image = Image.open(file_name) | |
| # col1.image(image, use_column_width=True) | |
| # predictions = pipeline(image) | |
| # col2.header("Probabilities") | |
| # for p in predictions: | |
| # col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%") | |
| # # my first app | |
| # import streamlit as st | |
| # x = st.slider('Select a value') | |
| # st.write(x, 'squared is', x * x) |