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import gradio as gr
import matplotlib.pyplot as plt
import tempfile, cv2, dlib, imutils
import torch
from scipy.spatial import distance
from imutils import face_utils
def eye_aspect_ratio(eye):
A, B, C = [distance.euclidean(eye[i], eye[j]) for i, j in [(1, 5), (2, 4), (0, 3)]]
return (A + B) / (2.0 * C)
def detect_blinks(video_file):
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("assets/models/shape_predictor_68_face_landmarks.dat")
cap = cv2.VideoCapture(video_file)
ear_list, counter, total = [], 0, 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(imutils.resize(frame, width=500), cv2.COLOR_BGR2GRAY)
for rect in detector(gray, 0):
shape = face_utils.shape_to_np(predictor(gray, rect))
left_eye, right_eye = [shape[face_utils.FACIAL_LANDMARKS_IDXS[eye][0]:face_utils.FACIAL_LANDMARKS_IDXS[eye][1]] for eye in ["left_eye", "right_eye"]]
ear = sum(eye_aspect_ratio(eye) for eye in [left_eye, right_eye]) / 2.0
ear_list.append(ear)
if ear < 0.3:
counter += 1
elif counter >= 5:
total += 1
counter = 0
else:
counter = 0
cap.release()
plt.figure(figsize=(10, 4))
plt.plot(ear_list)
plt.title('Eye Aspect Ratio over Time')
plt.xlabel('Frame')
plt.ylabel('EAR')
plt.tight_layout()
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
plt.savefig(temp_file.name)
plt.close()
return total, temp_file.name
def create_blink_tab():
with gr.Row():
with gr.Column(scale=2):
input_video = gr.Video(label="Input Video")
with gr.Row():
clear_btn = gr.Button("Clear", scale=1)
submit_btn = gr.Button("Analyze", scale=1, elem_classes="submit")
with gr.Column(scale=1):
output_count = gr.Label(label="Blink Count")
output_plot = gr.Image(label="EAR Plot")
submit_btn.click(fn=detect_blinks, inputs=[input_video], outputs=[output_count, output_plot])
clear_btn.click(lambda: (None, None, None), outputs=[input_video, output_count, output_plot])
gr.Examples(["./assets/videos/fitness.mp4"], [input_video])
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