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# import streamlit as st
# from streamlit_webrtc import webrtc_streamer
# import torch
# torch.classes.__path__ = []
import sys
import os
from glob import glob
import gradio as gr
from fastrtc import WebRTC
from fastrtc import VideoStreamHandler
from PIL import Image
import landmark_detection
import numpy as np
from time import time
import cv2
from mtcnn_facedetection import detect_faces
from selfie_filter import apply_sunglasses, process_video
radius = 2
filter_img = None
def do_facial_landmark_recognition(
image: np.ndarray, face_boxes: list[landmark_detection.BoundingBox]
):
faces = landmark_detection.get_faces(image, face_boxes)
landmarks_batch = landmark_detection.get_landmarks(faces)
for i, landmarks in enumerate(landmarks_batch):
for landmark in landmarks:
image = cv2.circle(image, landmark, radius, (255, 0, 0), -1)
return image, landmarks_batch
def do_facial_landmark_recognition_with_mtcnn(image: np.ndarray):
face_boxes = detect_faces(image)
return do_facial_landmark_recognition(image, face_boxes)
def video_frame_callback_gradio(frame: np.array):
flipped = cv2.flip(frame, 1)
flipped, landmarks_batch = do_facial_landmark_recognition_with_mtcnn(flipped)
# Apply sunglasses filter
image = apply_sunglasses(flipped, landmarks_batch, filter_img)
return image # , AdditionalOutputs(flipped, flipped)
css = """.my-group {max-width: 600px !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important;}"""
image_extensions = [
"*.jpg",
"*.jpeg",
"*.png",
"*.gif",
"*.bmp",
"*.tiff",
"*.webp",
]
all_image_files = []
for ext in image_extensions:
pattern = os.path.join("images", "**", ext) # '**' for recursive search
image_files = glob(pattern, recursive=True)
all_image_files.extend(image_files)
all_image_files.sort()
with gr.Blocks(css=css) as demo:
with gr.Column(elem_classes=["my-column"]):
gr.HTML(
"""
<h1 style='text-align: center'>
Live Filter with FaceXFormer
</h1>
"""
)
with gr.Group(elem_classes=["my-group"]):
selected_filter = gr.Dropdown(
choices=all_image_files,
label="Choose filter",
value="images/sunglasses_1.png",
)
def change_filter(filter_path):
global filter_img
try:
filter_img = cv2.imread(filter_path, cv2.IMREAD_UNCHANGED)
except:
gr.Error("Error open" + filter_path)
change_filter(selected_filter.value)
selected_filter.change(
change_filter, inputs=[selected_filter], show_progress="full"
)
with gr.Group(elem_classes=["my-group"]):
stream = WebRTC(label="Stream", rtc_configuration=None)
stream.stream(
fn=VideoStreamHandler(
video_frame_callback_gradio, fps=12, skip_frames=True
),
inputs=[stream],
outputs=[stream],
time_limit=None,
)
with gr.Group(elem_classes=["my-group"]):
with gr.Column(elem_classes=["my-column"]):
gr.HTML(
"""
<h1 style='text-align: center'>
Or just apply the filter to a video
</h1>
"""
)
input_video = gr.Video(sources="upload", include_audio=False)
output_video = gr.Video(interactive=False, include_audio=False)
submit = gr.Button(variant="primary")
with gr.Column(elem_classes=["my-column"]):
submit.click(
lambda input_path: process_video(input_path, filter_img),
inputs=[input_video],
outputs=[output_video],
show_progress="full",
)
def test(times=10):
image = np.array(Image.open("tmp.jpg").resize((512, 512)))
# faces = ai.get_faces(image)
start = time()
frame_times = [None] * times
for i in range(times):
before = time()
do_facial_landmark_recognition_with_mtcnn(image)
after = time()
frame_times[i] = after - before
end = time()
print(f"Num Images: {times}")
print(f"Total time: {end - start}")
print(
f"Max frametime: {max(frame_times)}, FPS: {1 / max(frame_times)}",
)
print(
f"Min frametime: {min(frame_times)}, FPS: {1 / min(frame_times)}",
)
print(
f"Avg frametime: {sum(frame_times) / len(frame_times)}, FPS: {1 / (sum(frame_times) / len(frame_times))}",
)
if __name__ == "__main__":
no_params = 0
for name, i in landmark_detection.model.named_parameters(recurse=True):
no_params += i.numel()
print(name, i.numel())
print(no_params)
if "--test" in sys.argv:
test()
exit(0)
else:
demo.launch()
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