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import gradio as gr | |
import os | |
import cv2 | |
import face_recognition | |
from fastai.vision.all import load_learner | |
import time | |
model = load_learner("gaze-recognizer-v1.pkl") | |
def video_processing(video): | |
print(video) | |
start_time = time.time() | |
# Loop through the frames of the video | |
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 | |
try: | |
# your processing code here | |
gaze_percentage = on_camera / total * 100 | |
except Exception as e: | |
print(f"An error occurred while processing the video: {e}") | |
gaze_percentage = '000' | |
# 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}') | |
print(gaze_percentage) | |
return str(gaze_percentage) | |
demo = gr.Interface(fn = video_processing, | |
inputs= gr.Video(), | |
outputs = 'text' | |
) | |
if __name__ == "__main__": | |
demo.launch() | |