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import gradio as gr
import cv2
from extract_frames import ExtractFrames
from get_every_fram_path import getEveryFramPath
from main_emotion_classifier import process, process_single_image
from grapher import createGraph
def process_image(image):
# Process the image using your existing function
processed_image = process_single_image(image)
return processed_image
def process_video(video_path):
# Extract frames from the video and process them
output_dir = ExtractFrames(video_path)
frame_paths = getEveryFramPath(output_dir)
results, most_frequent_emotion = process(frame_paths)
# Create the emotion graphs from the results
createGraph('data/output/results.txt')
# Return paths to the three generated graphs
return [
'data/output/emotion_bar_plot.png',
'data/output/emotion_stem_plot.png',
'data/output/emotionAVG.png'
]
def gradio_interface(file):
if file is None:
return None, None
file_type = file.name.split('.')[-1].lower()
if file_type in ['jpg', 'jpeg', 'png', 'bmp']: # Image input
image = cv2.imread(file.name)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
processed_image = process_image(image)
return processed_image, None
elif file_type in ['mp4', 'avi', 'mov', 'wmv']: # Video input
graph_paths = process_video(file.name)
return None, graph_paths
else:
return None, None
# Set up the Gradio Interface
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.File(label="Upload Image or Video"),
outputs=[
gr.Image(type="numpy", label="Processed Image (for image uploads)"),
gr.Gallery(label="Emotion Distribution Graphs (for video uploads)", columns=3)
],
title="Face Emotion Recognition",
description="Upload an image or video to analyze emotions. For images, the result will show detected faces with emotions. For videos, it will provide graphs of emotion distribution."
)
# Launch the Gradio interface
if __name__ == "__main__":
iface.launch()
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