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*.png
*.mkv
*.mp4
.venv/

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#!/usr/bin/env python3
"""
Strip Photography / Slit Photography Implementation
A digital implementation of strip photography that captures a two-dimensional
image as a sequence of one-dimensional images over time.
"""
import argparse
import sys
import cv2
import numpy as np
from pathlib import Path
def extract_column_strip(video_path, x_column, output_path):
"""
Extract vertical strip at x_column from each frame of the video.
Args:
video_path: Path to input video file
x_column: X-coordinate of the column to extract
output_path: Path for output image
"""
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
raise ValueError(f"Could not open video file: {video_path}")
# Get video properties
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
if x_column >= frame_width:
raise ValueError(f"Column {x_column} is outside video width ({frame_width})")
print(f"Processing {total_frames} frames...")
print(f"Extracting column {x_column} from {frame_width}x{frame_height} frames")
# Initialize output array: (height, total_frames, 3)
strip_image = np.zeros((frame_height, total_frames, 3), dtype=np.uint8)
frame_idx = 0
while True:
ret, frame = cap.read()
if not ret:
break
if frame_idx < total_frames:
# Extract column at x_column and store in strip_image
strip_image[:, frame_idx, :] = frame[:, x_column, :]
frame_idx += 1
if frame_idx % 100 == 0:
print(f"Processed {frame_idx}/{total_frames} frames")
cap.release()
print(f"Output dimensions: {strip_image.shape}")
print(f"Saving to: {output_path}")
# Save the strip image
cv2.imwrite(str(output_path), strip_image)
def extract_row_strip(video_path, y_row, output_path):
"""
Extract horizontal strip at y_row from each frame of the video.
Args:
video_path: Path to input video file
y_row: Y-coordinate of the row to extract
output_path: Path for output image
"""
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
raise ValueError(f"Could not open video file: {video_path}")
# Get video properties
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
if y_row >= frame_height:
raise ValueError(f"Row {y_row} is outside video height ({frame_height})")
print(f"Processing {total_frames} frames...")
print(f"Extracting row {y_row} from {frame_width}x{frame_height} frames")
# Initialize output array: (total_frames, width, 3)
strip_image = np.zeros((total_frames, frame_width, 3), dtype=np.uint8)
frame_idx = 0
while True:
ret, frame = cap.read()
if not ret:
break
if frame_idx < total_frames:
# Extract row at y_row and store in strip_image
strip_image[frame_idx, :, :] = frame[y_row, :, :]
frame_idx += 1
if frame_idx % 100 == 0:
print(f"Processed {frame_idx}/{total_frames} frames")
cap.release()
print(f"Output dimensions: {strip_image.shape}")
print(f"Saving to: {output_path}")
# Save the strip image
cv2.imwrite(str(output_path), strip_image)
def main():
"""Main entry point for the strip photography tool."""
parser = argparse.ArgumentParser(
description="Extract strip photography effects from video files"
)
parser.add_argument(
"video_file",
help="Input video file path"
)
parser.add_argument(
"--xcolumn",
type=int,
help="Extract vertical line at x-coordinate (column mode)"
)
parser.add_argument(
"--yrow",
type=int,
help="Extract horizontal line at y-coordinate (row mode)"
)
parser.add_argument(
"--output",
required=True,
help="Output image file path"
)
args = parser.parse_args()
# Validate input file
video_path = Path(args.video_file)
if not video_path.exists():
print(f"Error: Video file not found: {video_path}")
sys.exit(1)
# Validate mode selection
if args.xcolumn is not None and args.yrow is not None:
print("Error: Cannot specify both --xcolumn and --yrow. Choose one mode.")
sys.exit(1)
if args.xcolumn is None and args.yrow is None:
print("Error: Must specify either --xcolumn or --yrow.")
sys.exit(1)
# Validate coordinates
if args.xcolumn is not None and args.xcolumn < 0:
print("Error: --xcolumn must be non-negative")
sys.exit(1)
if args.yrow is not None and args.yrow < 0:
print("Error: --yrow must be non-negative")
sys.exit(1)
output_path = Path(args.output)
try:
if args.xcolumn is not None:
print(f"Column mode: Extracting vertical line at x={args.xcolumn}")
extract_column_strip(video_path, args.xcolumn, output_path)
else:
print(f"Row mode: Extracting horizontal line at y={args.yrow}")
extract_row_strip(video_path, args.yrow, output_path)
print("Strip photography extraction completed successfully!")
except Exception as e:
print(f"Error: {e}")
sys.exit(1)
if __name__ == "__main__":
main()

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[project]
name = "linescan-camera"
version = "0.1.0"
description = "Extract linescan images from video files"
readme = "readme.md"
requires-python = ">=3.8"
dependencies = [
"opencv-python>=4.8.0",
"numpy>=1.21.0",
]
[project.scripts]
linescan = "main:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["."]
[tool.uv]
dev-dependencies = []

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# Strip Photography / Slit Photography
A digital implementation of **strip photography** (also called **slit photography**) that captures a two-dimensional image as a sequence of one-dimensional images over time.
**How it works:**
Strip photography records a moving scene over time using a camera that observes a narrow strip rather than the full field. This implementation simulates the technique by extracting the same line position from each video frame and assembling them into a composite image where:
- One axis represents **space** (the slit/line being observed)
- The other axis represents **time** (progression through video frames)
**Visual effects:**
- Moving objects appear as visible shapes in the final image
- Stationary objects (like background) appear as horizontal/vertical stripes
- Object width is inversely proportional to speed (faster = narrower, slower = wider)
## Usage
**Column Mode** - Extract vertical lines (columns) from each frame:
```bash
uv run main.py test1.mkv --xcolumn 100 --output test1_column.png
```
**Row Mode** - Extract horizontal lines (rows) from each frame:
```bash
uv run main.py test1.mkv --yrow 200 --output test1_row.png
```
## Configure
We use uv to handle pip dependencies. Install with:
```bash
uv sync
```
## Output
- **Column mode**: Extracts vertical line at x-coordinate from each frame
- Output dimensions: `(source_height, total_frames, 3)`
- Width = number of frames, Height = source video height
- **Row mode**: Extracts horizontal line at y-coordinate from each frame
- Output dimensions: `(total_frames, source_width, 3)`
- Width = source video width, Height = number of frames
Each column/row in the output represents one frame from the input video, showing motion over time.

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