import gradio as gr import os import cv2 import face_recognition from fastai.vision.all import * import time version = 1 model_path = f'models/gaze-recognizer-v{version}.pkl' def video_processing(video): start_time = time.time() # Loop through the frames of the video model = load_learner(model_path) video_capture = cv2.VideoCapture(video) on_camera = 0 off_camera = 0 total = 0 while True: # Read a single frame from the video for i in range(24*30): ret, frame = video_capture.read() if not ret: break # If there are no more frames, break out of the loop if not ret: break # Convert the frame to RGB color (face_recognition uses RGB) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Find all the faces in the frame using a pre-trained convolutional neural network. face_locations = face_recognition.face_locations(gray) #face_locations = face_recognition.face_locations(gray, number_of_times_to_upsample=0, model="cnn") if len(face_locations) > 0: # Show the original frame with face rectangles drawn around the faces for top, right, bottom, left in face_locations: # cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) face_image = gray[top:bottom, left:right] # Resize the face image to the desired size resized_face_image = cv2.resize(face_image, (128,128)) # Predict the class of the resized face image using the model result = model.predict(resized_face_image) print(result[0]) if(result[0] == 'on_camera'): on_camera = on_camera + 1 elif(result[0] == 'off_camera'): off_camera = off_camera + 1 total = total + 1 # cv2.imshow('Video', frame) # If the user presses the 'q' key, exit the loop # if cv2.waitKey(1) & 0xFF == ord('q'): # break gaze_percentage = on_camera/total*100 # print(total,on_camera,off_camera) # print(f'focus perfectage = {on_camera/total*100}') # Release the video capture object and close all windows video_capture.release() cv2.destroyAllWindows() end_time = time.time() print(f'Time taken: {end_time-start_time}') return gaze_percentage demo = gr.Interface(fn = video_processing, inputs= gr.Video(), outputs = gr.Text() ) if __name__ == "__main__": demo.launch()