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import os
import time
import cv2
import imutils
import shutil
import img2pdf
import glob
from skimage.metrics import structural_similarity
import gradio as gr
import tempfile
import whisper
from moviepy.editor import VideoFileClip
from PIL import Image, ImageDraw, ImageFont
############# Define constants
OUTPUT_SLIDES_DIR = f"./output"
FRAME_RATE = 3 # no.of frames per second that needs to be processed, fewer the count faster the speed
WARMUP = FRAME_RATE # initial number of frames to be skipped
FGBG_HISTORY = FRAME_RATE * 15 # no.of frames in background object
VAR_THRESHOLD = 16 # Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model.
DETECT_SHADOWS = False # If true, the algorithm will detect shadows and mark them.
MIN_PERCENT = 0.1 # min % of diff between foreground and background to detect if motion has stopped
MAX_PERCENT = 3 # max % of diff between foreground and background to detect if frame is still in motion
SSIM_THRESHOLD = 0.9 # SSIM threshold of two consecutive frame
def get_frames(video_path):
'''A fucntion to return the frames from a video located at video_path
this function skips frames as defined in FRAME_RATE'''
# open a pointer to the video file initialize the width and height of the frame
vs = cv2.VideoCapture(video_path)
if not vs.isOpened():
raise Exception(f'unable to open file {video_path}')
total_frames = vs.get(cv2.CAP_PROP_FRAME_COUNT)
frame_time = 0
frame_count = 0
# loop over the frames of the video
while True:
vs.set(cv2.CAP_PROP_POS_MSEC, frame_time * 1000) # move frame to a timestamp
frame_time += 1/FRAME_RATE
(_, frame) = vs.read()
# if the frame is None, then we have reached the end of the video file
if frame is None:
break
frame_count += 1
yield frame_count, frame_time, frame
vs.release()
def detect_unique_screenshots(video_path, output_folder_screenshot_path, progress=gr.Progress()):
'''Extract unique screenshots from video'''
fgbg = cv2.createBackgroundSubtractorMOG2(history=FGBG_HISTORY, varThreshold=VAR_THRESHOLD,detectShadows=DETECT_SHADOWS)
captured = False
start_time = time.time()
(W, H) = (None, None)
# Get total frames for progress calculation
cap = cv2.VideoCapture(video_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
cap.release()
screenshoots_count = 0
last_screenshot = None
saved_files = []
progress(0, desc="初始化视频处理...")
for frame_count, frame_time, frame in get_frames(video_path):
# Update progress
progress((frame_count / total_frames) * 0.7, desc=f"处理视频帧 {frame_count}/{total_frames}")
orig = frame.copy()
frame = imutils.resize(frame, width=600)
mask = fgbg.apply(frame)
if W is None or H is None:
(H, W) = mask.shape[:2]
p_diff = (cv2.countNonZero(mask) / float(W * H)) * 100
if p_diff < MIN_PERCENT and not captured and frame_count > WARMUP:
captured = True
filename = f"{screenshoots_count:03}_{round(frame_time/60, 2)}.png"
path = os.path.join(output_folder_screenshot_path, filename)
image_ssim = 0.0
if last_screenshot is not None:
image_ssim = structural_similarity(last_screenshot, orig, channel_axis=2, data_range=255)
if image_ssim < SSIM_THRESHOLD:
try:
progress(0.7 + (screenshoots_count * 0.1), desc=f"保存截图 {screenshoots_count + 1}")
print("saving {}".format(path))
cv2.imwrite(str(path), orig)
last_screenshot = orig
saved_files.append(path)
screenshoots_count += 1
except Exception as e:
print(f"Error saving image: {str(e)}")
continue
elif captured and p_diff >= MAX_PERCENT:
captured = False
progress(0.8, desc="截图提取完成")
print(f'{screenshoots_count} screenshots Captured!')
print(f'Time taken {time.time()-start_time}s')
return saved_files
def initialize_output_folder(video_path):
'''Clean the output folder if already exists'''
# Create a safe folder name from video filename
video_filename = os.path.splitext(os.path.basename(video_path))[0]
# Replace potentially problematic characters
safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
output_folder_screenshot_path = os.path.join(OUTPUT_SLIDES_DIR, safe_filename)
if os.path.exists(output_folder_screenshot_path):
shutil.rmtree(output_folder_screenshot_path)
os.makedirs(output_folder_screenshot_path, exist_ok=True)
print('initialized output folder', output_folder_screenshot_path)
return output_folder_screenshot_path
def convert_screenshots_to_pdf(video_path, output_folder_screenshot_path):
# Create a safe filename
video_filename = os.path.splitext(os.path.basename(video_path))[0]
safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
output_pdf_path = os.path.join(OUTPUT_SLIDES_DIR, f"{safe_filename}.pdf")
try:
print('output_folder_screenshot_path', output_folder_screenshot_path)
print('output_pdf_path', output_pdf_path)
print('converting images to pdf..')
# Get all PNG files and ensure they exist
png_files = sorted(glob.glob(os.path.join(output_folder_screenshot_path, "*.png")))
if not png_files:
raise Exception("No PNG files found to convert to PDF")
with open(output_pdf_path, "wb") as f:
f.write(img2pdf.convert(png_files))
print('Pdf Created!')
print('pdf saved at', output_pdf_path)
return output_pdf_path
except Exception as e:
print(f"Error creating PDF: {str(e)}")
raise
def video_to_slides(video_path, progress=gr.Progress()):
progress(0.1, desc="准备处理视频...")
output_folder_screenshot_path = initialize_output_folder(video_path)
saved_files = detect_unique_screenshots(video_path, output_folder_screenshot_path, progress)
return output_folder_screenshot_path, saved_files
def slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress=gr.Progress()):
video_filename = os.path.splitext(os.path.basename(video_path))[0]
safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
output_pdf_path = os.path.join(OUTPUT_SLIDES_DIR, f"{safe_filename}.pdf")
try:
progress(0.9, desc="正在生成PDF...")
print('output_folder_screenshot_path', output_folder_screenshot_path)
print('output_pdf_path', output_pdf_path)
if not saved_files:
raise Exception("未从视频中捕获到截图")
existing_files = [f for f in saved_files if os.path.exists(f)]
if not existing_files:
raise Exception("未找到保存的截图文件")
with open(output_pdf_path, "wb") as f:
f.write(img2pdf.convert(existing_files))
progress(1.0, desc="处理完成!")
print('PDF创建成功!')
print('PDF保存位置:', output_pdf_path)
return output_pdf_path
except Exception as e:
print(f"创建PDF时出错: {str(e)}")
raise
def run_app(video_path, progress=gr.Progress()):
try:
if not video_path:
raise gr.Error("请选择要处理的视频文件")
progress(0, desc="开始处理...")
output_folder_screenshot_path, saved_files = video_to_slides(video_path, progress)
return slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress)
except Exception as e:
raise gr.Error(f"处理失败: {str(e)}")
def process_video_file(video_file):
"""Handle uploaded video file and return PDF"""
try:
# If video_file is a string (path), use it directly
if isinstance(video_file, str):
if video_file.strip() == "":
return None
return run_app(video_file)
# If it's an uploaded file, create a temporary file
if video_file is not None:
# Generate a unique filename for the temporary video
temp_filename = f"temp_video_{int(time.time())}.mp4"
temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
try:
if hasattr(video_file, 'name'): # If it's already a file path
shutil.copyfile(video_file, temp_path)
else: # If it's file content
with open(temp_path, 'wb') as f:
f.write(video_file)
# Process the video
output_folder_screenshot_path, saved_files = video_to_slides(temp_path)
pdf_path = slides_to_pdf(temp_path, output_folder_screenshot_path, saved_files)
# Cleanup
if os.path.exists(temp_path):
os.unlink(temp_path)
return pdf_path
except Exception as e:
if os.path.exists(temp_path):
os.unlink(temp_path)
raise gr.Error(f"处理视频时出错: {str(e)}")
return None
except Exception as e:
raise gr.Error(f"处理视频时出错: {str(e)}")
def extract_audio_and_transcribe(video_path, progress=gr.Progress()):
"""Extract audio from video and transcribe it using Whisper"""
progress(0, desc="正在提取音频...")
# Load the video and extract audio
video = VideoFileClip(video_path)
audio = video.audio
# Save audio to temporary file
temp_audio = tempfile.mktemp(suffix='.wav')
audio.write_audiofile(temp_audio)
progress(0.3, desc="正在转录音频...")
# Load Whisper model and transcribe
model = whisper.load_model("base")
result = model.transcribe(temp_audio)
print("完成的转录文本结果如下:"+result)
# Clean up
os.remove(temp_audio)
video.close()
# Process segments with timestamps
segments = []
for segment in result["segments"]:
segments.append({
"start": segment["start"],
"end": segment["end"],
"text": segment["text"].strip()
})
return segments
def add_text_to_image(image_path, text):
"""Add text below the image"""
# Open image
img = Image.open(image_path)
width, height = img.size
# Create new image with space for text
font_size = 30
font = ImageFont.truetype("arial.ttf", font_size)
text_height = font_size * (text.count('\n') + 2) # Add padding
new_img = Image.new('RGB', (width, height + text_height), 'white')
new_img.paste(img, (0, 0))
# Add text
draw = ImageDraw.Draw(new_img)
draw.text((10, height + 10), text, font=font, fill='black')
# Save the modified image
new_img.save(image_path)
def process_video_with_transcription(video_path, output_folder_screenshot_path, progress=gr.Progress()):
"""Process video with transcription and add text to images"""
# First, get the transcription
segments = extract_audio_and_transcribe(video_path, progress)
# Then get the frames as before
saved_files = detect_unique_screenshots(video_path, output_folder_screenshot_path, progress)
progress(0.8, desc="正在添加字幕...")
# Match transcription segments with images
for i, image_path in enumerate(saved_files):
# Extract timestamp from filename (format: 000_1.23.png)
timestamp = float(os.path.basename(image_path).split('_')[1].split('.png')[0])
# Find relevant text segments for this timestamp
relevant_text = []
for segment in segments:
if segment["start"] <= timestamp * 60 <= segment["end"]:
relevant_text.append(segment["text"])
# Add text to image
if relevant_text:
text = "\n".join(relevant_text)
add_text_to_image(image_path, text)
progress(0.9, desc="处理完成...")
return saved_files
def run_app_with_transcription(video_path, progress=gr.Progress()):
try:
if not video_path:
raise gr.Error("请选择要处理的视频文件")
progress(0, desc="开始处理...")
output_folder_screenshot_path = initialize_output_folder(video_path)
saved_files = process_video_with_transcription(video_path, output_folder_screenshot_path, progress)
return slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress)
except Exception as e:
raise gr.Error(f"处理失败: {str(e)}")
def process_video_file_with_transcription(video_file):
"""Handle uploaded video file and return PDF with transcription"""
try:
# If video_file is a string (path), use it directly
if isinstance(video_file, str):
if video_file.strip() == "":
return None
return run_app_with_transcription(video_file)
# If it's an uploaded file, create a temporary file
if video_file is not None:
# Generate a unique filename for the temporary video
temp_filename = f"temp_video_{int(time.time())}.mp4"
temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
try:
if hasattr(video_file, 'name'): # If it's already a file path
shutil.copyfile(video_file, temp_path)
else: # If it's file content
with open(temp_path, 'wb') as f:
f.write(video_file)
# Process the video
output_folder_screenshot_path, saved_files = video_to_slides(temp_path)
saved_files = process_video_with_transcription(temp_path, output_folder_screenshot_path)
pdf_path = slides_to_pdf(temp_path, output_folder_screenshot_path, saved_files)
# Cleanup
if os.path.exists(temp_path):
os.unlink(temp_path)
return pdf_path
except Exception as e:
if os.path.exists(temp_path):
os.unlink(temp_path)
raise gr.Error(f"处理视频时出错: {str(e)}")
return None
except Exception as e:
raise gr.Error(f"处理视频时出错: {str(e)}")
def process_video(video, path):
if video:
return run_app(video)
elif path:
return run_app(path)
else:
raise gr.Error("请上传视频或输入视频路径")
def handle_video_with_transcription(video, path):
if video:
return run_app_with_transcription(video)
elif path:
return run_app_with_transcription(path)
else:
raise gr.Error("请上传视频或输入视频路径")
# Create a modern interface with custom CSS
css = """
.gradio-container {
font-family: 'SF Pro Display', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
}
.container {
max-width: 900px;
margin: auto;
padding: 20px;
}
.gr-button {
background: linear-gradient(90deg, #2563eb, #3b82f6);
border: none;
color: white;
}
.gr-button:hover {
background: linear-gradient(90deg, #1d4ed8, #2563eb);
transform: translateY(-1px);
box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
}
.status-info {
margin-top: 10px;
padding: 10px;
border-radius: 4px;
background-color: #f3f4f6;
}
"""
if __name__ == "__main__":
with gr.Blocks(css=css) as iface:
gr.Markdown("# 视频转PDF工具")
with gr.Tab("基础转换"):
with gr.Row():
with gr.Column():
video_input = gr.Video(label="上传视频")
video_path = gr.Textbox(label="或输入视频路径", placeholder="例如: ./input/video.mp4")
convert_btn = gr.Button("开始转换", variant="primary")
with gr.Row():
output_file = gr.File(label="下载PDF")
with gr.Tab("带语音转文字"):
with gr.Row():
with gr.Column():
video_input_with_transcription = gr.Video(label="上传视频")
video_path_with_transcription = gr.Textbox(label="或输入视频路径", placeholder="例如: ./input/video.mp4")
convert_btn_with_transcription = gr.Button("开始转换(带字幕)", variant="primary")
with gr.Row():
output_file_with_transcription = gr.File(label="下载PDF(带字幕)")
convert_btn.click(
fn=process_video,
inputs=[video_input, video_path],
outputs=[output_file],
)
convert_btn_with_transcription.click(
fn=handle_video_with_transcription,
inputs=[video_input_with_transcription, video_path_with_transcription],
outputs=[output_file_with_transcription],
)
iface.launch()
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