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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(f'Total = {total},on_camera = {on_camera},off_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()
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