def read_text_file(file_path):
with open(file_path, 'r') as f:
text = f.read()
return text
import cv2
from PIL import Image
import pytesseract
def process_video(video_path):
cap = cv2.VideoCapture(video_path)
while True:
ret, frame = cap.read()
if not ret:
break
# Convert frame to text using OCR
text = pytesseract.image_to_string(Image.fromarray(frame))
# Process the text
print(text)
cap.release()
def analyze_video_with_text(video_path, text_path):
video_text = process_video(video_path)
file_text = read_text_file(text_path)
# Combine or compare video_text and file_text as needed
API Development (Optional): If you want to expose this functionality as an API:
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/analyze', methods=['POST'])
def analyze():
video_file = request.files['video']
text_file = request.files['text']
# Save files and call analyze_video_with_text
# Return the result as JSON
Testing:
This example provides a very high-level overview. Depending on your specific requirements (e.g., what you want to do with "SS Maisie Video 05" and its associated text file), you'll need to adjust the implementation details significantly. SS Maisie Video 05 txt
If you provide more specifics about what you're trying to achieve, a more detailed and accurate plan could be developed. import cv2 from PIL import Image import pytesseract
The wreck is located at the Abu Nuhas reef system, often referred to as the "Ship Graveyard." It sits upright on the seabed. possibly for tasks such as:
The objective here could be to develop a feature that can handle video files (like "SS Maisie Video 05") and associated text files (like "txt" files), possibly for tasks such as:
Example layout: 00:00 — [MAISIE] Intro: "…" 00:12 — [SFX: door close] 00:14 — [MAISIE] "…" (Continue until end)