DeepSwapFace / utils.py
Harisreedhar
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import os
import cv2
import time
import glob
import shutil
import platform
import datetime
import subprocess
from threading import Thread
from moviepy.editor import VideoFileClip, ImageSequenceClip
from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
def trim_video(video_path, output_path, start_frame, stop_frame):
video_name, _ = os.path.splitext(os.path.basename(video_path))
trimmed_video_filename = video_name + "_trimmed" + ".mp4"
temp_path = os.path.join(output_path, "trim")
os.makedirs(temp_path, exist_ok=True)
trimmed_video_file_path = os.path.join(temp_path, trimmed_video_filename)
video = VideoFileClip(video_path)
fps = video.fps
start_time = start_frame / fps
duration = (stop_frame - start_frame) / fps
trimmed_video = video.subclip(start_time, start_time + duration)
trimmed_video.write_videofile(
trimmed_video_file_path, codec="libx264", audio_codec="aac"
)
trimmed_video.close()
video.close()
return trimmed_video_file_path
def open_directory(path=None):
if path is None:
return
try:
os.startfile(path)
except:
subprocess.Popen(["xdg-open", path])
class StreamerThread(object):
def __init__(self, src=0):
self.capture = cv2.VideoCapture(src)
self.capture.set(cv2.CAP_PROP_BUFFERSIZE, 2)
self.FPS = 1 / 30
self.FPS_MS = int(self.FPS * 1000)
self.thread = None
self.stopped = False
self.frame = None
def start(self):
self.thread = Thread(target=self.update, args=())
self.thread.daemon = True
self.thread.start()
def stop(self):
self.stopped = True
self.thread.join()
print("stopped")
def update(self):
while not self.stopped:
if self.capture.isOpened():
(self.status, self.frame) = self.capture.read()
time.sleep(self.FPS)
class ProcessBar:
def __init__(self, bar_length, total, before="⬛", after="🟨"):
self.bar_length = bar_length
self.total = total
self.before = before
self.after = after
self.bar = [self.before] * bar_length
self.start_time = time.time()
def get(self, index):
total = self.total
elapsed_time = time.time() - self.start_time
average_time_per_iteration = elapsed_time / (index + 1)
remaining_iterations = total - (index + 1)
estimated_remaining_time = remaining_iterations * average_time_per_iteration
self.bar[int(index / total * self.bar_length)] = self.after
info_text = f"({index+1}/{total}) {''.join(self.bar)} "
info_text += f"(ETR: {int(estimated_remaining_time // 60)} min {int(estimated_remaining_time % 60)} sec)"
return info_text
logo_image = cv2.imread("./assets/images/logo.png", cv2.IMREAD_UNCHANGED)
def add_logo_to_image(img, logo=logo_image):
logo_size = int(img.shape[1] * 0.1)
logo = cv2.resize(logo, (logo_size, logo_size))
if logo.shape[2] == 4:
alpha = logo[:, :, 3]
else:
alpha = np.ones_like(logo[:, :, 0]) * 255
padding = int(logo_size * 0.1)
roi = img.shape[0] - logo_size - padding, img.shape[1] - logo_size - padding
for c in range(0, 3):
img[roi[0] : roi[0] + logo_size, roi[1] : roi[1] + logo_size, c] = (
alpha / 255.0
) * logo[:, :, c] + (1 - alpha / 255.0) * img[
roi[0] : roi[0] + logo_size, roi[1] : roi[1] + logo_size, c
]
return img
def split_list_by_lengths(data, length_list):
split_data = []
start_idx = 0
for length in length_list:
end_idx = start_idx + length
sublist = data[start_idx:end_idx]
split_data.append(sublist)
start_idx = end_idx
return split_data
def merge_img_sequence_from_ref(ref_video_path, image_sequence, output_file_name):
video_clip = VideoFileClip(ref_video_path)
fps = video_clip.fps
duration = video_clip.duration
total_frames = video_clip.reader.nframes
audio_clip = video_clip.audio if video_clip.audio is not None else None
edited_video_clip = ImageSequenceClip(image_sequence, fps=fps)
if audio_clip is not None:
edited_video_clip = edited_video_clip.set_audio(audio_clip)
edited_video_clip.set_duration(duration).write_videofile(
output_file_name, codec="libx264"
)
edited_video_clip.close()
video_clip.close()
def scale_bbox_from_center(bbox, scale_width, scale_height, image_width, image_height):
# Extract the coordinates of the bbox
x1, y1, x2, y2 = bbox
# Calculate the center point of the bbox
center_x = (x1 + x2) / 2
center_y = (y1 + y2) / 2
# Calculate the new width and height of the bbox based on the scaling factors
width = x2 - x1
height = y2 - y1
new_width = width * scale_width
new_height = height * scale_height
# Calculate the new coordinates of the bbox, considering the image boundaries
new_x1 = center_x - new_width / 2
new_y1 = center_y - new_height / 2
new_x2 = center_x + new_width / 2
new_y2 = center_y + new_height / 2
# Adjust the coordinates to ensure the bbox remains within the image boundaries
new_x1 = max(0, new_x1)
new_y1 = max(0, new_y1)
new_x2 = min(image_width - 1, new_x2)
new_y2 = min(image_height - 1, new_y2)
# Return the scaled bbox coordinates
scaled_bbox = [new_x1, new_y1, new_x2, new_y2]
return scaled_bbox