add threshold

This commit is contained in:
5shekel 2025-10-26 10:12:28 +02:00
parent be3eddb3a6
commit 2e531e478a
3 changed files with 807 additions and 19 deletions

266
main.py
View File

@ -10,17 +10,163 @@ import argparse
import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
def extract_column_strip(video_path, x_column, output_path):
def calculate_line_difference(line1, line2):
"""
Calculate the difference between two lines (column or row).
Args:
line1, line2: numpy arrays representing lines from consecutive frames
Returns:
float: normalized difference value between 0 and 1
"""
# Convert to float for calculation
diff = np.abs(line1.astype(np.float32) - line2.astype(np.float32))
# Calculate mean difference across all channels
mean_diff = np.mean(diff)
# Normalize to 0-255 range
return mean_diff / 255.0
def generate_change_graph(changes, output_path, threshold=None):
"""
Generate a graph showing change values over time.
Args:
changes: List of change values
output_path: Path for output graph image
threshold: Optional threshold line to display
"""
plt.figure(figsize=(12, 6))
plt.plot(changes, linewidth=1, alpha=0.7)
plt.xlabel('Frame Number')
plt.ylabel('Change Value (0-1)')
plt.title('Line Change Detection Over Time')
plt.grid(True, alpha=0.3)
if threshold is not None:
plt.axhline(y=threshold, color='r', linestyle='--',
label=f'Threshold: {threshold:.3f}')
plt.legend()
# Add statistics
mean_change = np.mean(changes)
max_change = np.max(changes)
std_change = np.std(changes)
stats_text = f'Mean: {mean_change:.3f}\nMax: {max_change:.3f}\nStd: {std_change:.3f}'
plt.text(0.02, 0.98, stats_text, transform=plt.gca().transAxes,
verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
plt.tight_layout()
plt.savefig(output_path, dpi=150, bbox_inches='tight')
plt.close()
print(f"Change graph saved to: {output_path}")
def analyze_changes_only(video_path, x_column=None, y_row=None, debug_output=None):
"""
Analyze changes in video without generating strip image.
Used for debug mode to generate change threshold graphs.
Args:
video_path: Path to input video file
x_column: X-coordinate of column to analyze (if column mode)
y_row: Y-coordinate of row to analyze (if row mode)
debug_output: Base path for debug outputs
Returns:
List of change values
"""
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 is not None:
if x_column >= frame_width:
raise ValueError(f"Column {x_column} is outside video width ({frame_width})")
print(f"Analyzing column {x_column} from {frame_width}x{frame_height} frames")
else:
if y_row >= frame_height:
raise ValueError(f"Row {y_row} is outside video height ({frame_height})")
print(f"Analyzing row {y_row} from {frame_width}x{frame_height} frames")
print(f"Processing {total_frames} frames for change analysis...")
changes = []
previous_line = None
frame_idx = 0
while True:
ret, frame = cap.read()
if not ret:
break
# Extract current line (column or row)
if x_column is not None:
current_line = frame[:, x_column, :].copy()
else:
current_line = frame[y_row, :, :].copy()
# Calculate change from previous frame
if previous_line is not None:
change = calculate_line_difference(current_line, previous_line)
changes.append(change)
previous_line = current_line
frame_idx += 1
if frame_idx % 100 == 0:
print(f"Analyzed {frame_idx}/{total_frames} frames")
cap.release()
if debug_output:
# Generate change graph
graph_path = f"{debug_output}_changes.png"
generate_change_graph(changes, graph_path)
# Generate statistics
if changes:
print(f"\nChange Analysis Statistics:")
print(f"Total frames analyzed: {len(changes)}")
print(f"Mean change: {np.mean(changes):.4f}")
print(f"Max change: {np.max(changes):.4f}")
print(f"Min change: {np.min(changes):.4f}")
print(f"Std deviation: {np.std(changes):.4f}")
# Suggest thresholds
percentiles = [50, 75, 90, 95, 99]
print(f"\nSuggested threshold values:")
for p in percentiles:
thresh = np.percentile(changes, p)
frames_above = np.sum(np.array(changes) >= thresh)
compression = (len(changes) - frames_above) / len(changes) * 100
print(f" {p}th percentile: {thresh:.4f} (keeps {frames_above} frames, {compression:.1f}% compression)")
return changes
def extract_column_strip(video_path, x_column, output_path, change_threshold=0.01):
"""
Extract vertical strip at x_column from each frame of the video.
Only include frames where the change exceeds the threshold.
Args:
video_path: Path to input video file
x_column: X-coordinate of the column to extract
output_path: Path for output image
change_threshold: Minimum change threshold (0-1) to include frame
"""
cap = cv2.VideoCapture(str(video_path))
@ -37,9 +183,13 @@ def extract_column_strip(video_path, x_column, output_path):
print(f"Processing {total_frames} frames...")
print(f"Extracting column {x_column} from {frame_width}x{frame_height} frames")
print(f"Change threshold: {change_threshold}")
# Initialize output array: (height, total_frames, 3)
strip_image = np.zeros((frame_height, total_frames, 3), dtype=np.uint8)
# Collect significant columns
significant_columns = []
previous_column = None
included_frames = 0
skipped_frames = 0
frame_idx = 0
while True:
@ -47,9 +197,24 @@ def extract_column_strip(video_path, x_column, output_path):
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, :]
# Extract current column
current_column = frame[:, x_column, :].copy()
# Check if this is the first frame or if change is significant
include_frame = False
if previous_column is None:
include_frame = True # Always include first frame
else:
change = calculate_line_difference(current_column, previous_column)
if change >= change_threshold:
include_frame = True
if include_frame:
significant_columns.append(current_column)
previous_column = current_column
included_frames += 1
else:
skipped_frames += 1
frame_idx += 1
if frame_idx % 100 == 0:
@ -57,6 +222,16 @@ def extract_column_strip(video_path, x_column, output_path):
cap.release()
if not significant_columns:
raise ValueError("No significant changes detected. Try lowering the threshold.")
# Convert list to numpy array
strip_image = np.stack(significant_columns, axis=1)
print(f"Original frames: {total_frames}")
print(f"Included frames: {included_frames}")
print(f"Skipped frames: {skipped_frames}")
print(f"Compression ratio: {skipped_frames/total_frames:.1%}")
print(f"Output dimensions: {strip_image.shape}")
print(f"Saving to: {output_path}")
@ -64,14 +239,16 @@ def extract_column_strip(video_path, x_column, output_path):
cv2.imwrite(str(output_path), strip_image)
def extract_row_strip(video_path, y_row, output_path):
def extract_row_strip(video_path, y_row, output_path, change_threshold=0.01):
"""
Extract horizontal strip at y_row from each frame of the video.
Only include frames where the change exceeds the threshold.
Args:
video_path: Path to input video file
y_row: Y-coordinate of the row to extract
output_path: Path for output image
change_threshold: Minimum change threshold (0-1) to include frame
"""
cap = cv2.VideoCapture(str(video_path))
@ -88,9 +265,13 @@ def extract_row_strip(video_path, y_row, output_path):
print(f"Processing {total_frames} frames...")
print(f"Extracting row {y_row} from {frame_width}x{frame_height} frames")
print(f"Change threshold: {change_threshold}")
# Initialize output array: (total_frames, width, 3)
strip_image = np.zeros((total_frames, frame_width, 3), dtype=np.uint8)
# Collect significant rows
significant_rows = []
previous_row = None
included_frames = 0
skipped_frames = 0
frame_idx = 0
while True:
@ -98,9 +279,24 @@ def extract_row_strip(video_path, y_row, output_path):
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, :, :]
# Extract current row
current_row = frame[y_row, :, :].copy()
# Check if this is the first frame or if change is significant
include_frame = False
if previous_row is None:
include_frame = True # Always include first frame
else:
change = calculate_line_difference(current_row, previous_row)
if change >= change_threshold:
include_frame = True
if include_frame:
significant_rows.append(current_row)
previous_row = current_row
included_frames += 1
else:
skipped_frames += 1
frame_idx += 1
if frame_idx % 100 == 0:
@ -108,6 +304,16 @@ def extract_row_strip(video_path, y_row, output_path):
cap.release()
if not significant_rows:
raise ValueError("No significant changes detected. Try lowering the threshold.")
# Convert list to numpy array
strip_image = np.stack(significant_rows, axis=0)
print(f"Original frames: {total_frames}")
print(f"Included frames: {included_frames}")
print(f"Skipped frames: {skipped_frames}")
print(f"Compression ratio: {skipped_frames/total_frames:.1%}")
print(f"Output dimensions: {strip_image.shape}")
print(f"Saving to: {output_path}")
@ -144,6 +350,19 @@ def main():
help="Output image file path"
)
parser.add_argument(
"--threshold",
type=float,
default=0.01,
help="Change threshold (0-1) for including frames (default: 0.01)"
)
parser.add_argument(
"--debug",
action="store_true",
help="Debug mode: analyze changes and generate threshold graph without creating strip image"
)
args = parser.parse_args()
# Validate input file
@ -170,15 +389,34 @@ def main():
print("Error: --yrow must be non-negative")
sys.exit(1)
# Validate threshold
if not (0 <= args.threshold <= 1):
print("Error: --threshold must be between 0 and 1")
sys.exit(1)
output_path = Path(args.output)
try:
if args.debug:
# Debug mode: analyze changes only
print("Debug mode: Analyzing changes and generating threshold graph")
if args.xcolumn is not None:
print(f"Column mode: Analyzing vertical line at x={args.xcolumn}")
analyze_changes_only(video_path, x_column=args.xcolumn, debug_output=output_path.stem)
else:
print(f"Row mode: Analyzing horizontal line at y={args.yrow}")
analyze_changes_only(video_path, y_row=args.yrow, debug_output=output_path.stem)
print("Change analysis completed successfully!")
else:
# Normal mode: extract strip photography
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)
extract_column_strip(video_path, args.xcolumn, output_path, args.threshold)
else:
print(f"Row mode: Extracting horizontal line at y={args.yrow}")
extract_row_strip(video_path, args.yrow, output_path)
extract_row_strip(video_path, args.yrow, output_path, args.threshold)
print("Strip photography extraction completed successfully!")

View File

@ -7,6 +7,7 @@ requires-python = ">=3.8"
dependencies = [
"opencv-python>=4.8.0",
"numpy>=1.21.0",
"matplotlib>=3.5.0",
]
[project.scripts]

549
uv.lock generated
View File

@ -16,11 +16,357 @@ resolution-markers = [
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]
[[package]]
name = "contourpy"
version = "1.1.1"
source = { registry = "https://pypi.org/simple" }
resolution-markers = [
"python_full_version < '3.9'",
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"python_full_version == '3.9.*' and platform_machine == 'aarch64' and platform_system == 'Linux'",
"(python_full_version == '3.9.*' and platform_machine != 'aarch64' and platform_machine != 'arm64') or (python_full_version == '3.9.*' and platform_machine == 'aarch64' and platform_system != 'Linux') or (python_full_version == '3.9.*' and platform_machine == 'arm64' and platform_system != 'Darwin')",
"python_full_version == '3.10.*' and platform_system == 'Darwin'",
"python_full_version == '3.10.*' and platform_machine == 'aarch64' and platform_system == 'Linux'",
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"python_full_version == '3.11.*' and platform_system == 'Darwin'",
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux'",
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux')",
]
dependencies = [
{ name = "numpy", marker = "python_full_version < '3.12'" },
]
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