Spaces:
Runtime error
Runtime error
| # import the necessary packages | |
| import face_recognition | |
| import pickle | |
| import argparse | |
| import cv2 | |
| from sklearn.neighbors import KNeighborsClassifier | |
| import gradio as gr | |
| import numpy as np | |
| def inf(_, image): | |
| # input_image = 'multiple.jpg' | |
| # encodings_file = 'encodings.pkl' | |
| detection_method = 'hog' | |
| classifier_model_file = 'model.pkl' | |
| labels_file = 'labels.pkl' | |
| # data = pickle.loads(open(encodings_file, "rb").read()) | |
| # image = cv2.imread(input_image) | |
| rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| boxes = face_recognition.face_locations(rgb_image, model=detection_method) | |
| encodings = face_recognition.face_encodings(rgb_image, boxes) | |
| print(f'Found {len(boxes)} faces') | |
| if len(boxes) == 0: | |
| return image | |
| # initialize the list of names for each face detected | |
| names = [] | |
| # # load the model from disk | |
| model = pickle.loads(open(classifier_model_file, "rb").read()) | |
| # predict on the new encodings and display the probabilities | |
| y_pred = model.predict_proba(encodings) | |
| # print(y_pred) | |
| max_y = y_pred.max(axis=1) | |
| print(max_y) | |
| labels = pickle.loads(open(labels_file, "rb").read()) | |
| for id, m in enumerate(max_y): | |
| if m >= 0.8: | |
| names.append(labels[y_pred.argmax(axis=1)[id]]) | |
| else: | |
| names.append('Unknown') | |
| # loop over the recognized faces | |
| for ((top, right, bottom, left), name) in zip(boxes, names): | |
| # draw the predicted face name on the image | |
| cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2) | |
| y = top - 15 if top - 15 > 15 else top + 15 | |
| cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX, | |
| 0.75, (0, 255, 0), 2) | |
| # save the resulting image | |
| # cv2.imwrite('output.jpg', image) | |
| return image | |
| demo = gr.Interface( | |
| inf, | |
| [ | |
| gr.Markdown(""" | |
| ## Welcome to the face recognition software !!! | |
| """), | |
| gr.Image(source="webcam", streaming=True)], | |
| "image", | |
| live=True | |
| ) | |
| demo.launch(server_name="0.0.0.0", share=True) | |