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Maybe implement a feature tracker

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This commit is contained in:
PhatPhuckDave
2025-09-26 13:10:47 +02:00
parent 13fbc45b74
commit 710a1f7de3
2 changed files with 314 additions and 4 deletions
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261
croppa/main.py
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@@ -4,7 +4,7 @@ import cv2
import argparse
import numpy as np
from pathlib import Path
from typing import List
from typing import List, Optional, Tuple, Dict, Any
import time
import re
import threading
@@ -25,6 +25,173 @@ def load_image_utf8(image_path):
except Exception as e:
raise ValueError(f"Could not load image file: {image_path} - {e}")
class FeatureTracker:
"""Semi-automatic feature tracking with SIFT/SURF/ORB support and full state serialization"""
def __init__(self):
# Feature detection parameters
self.detector_type = 'SIFT' # 'SIFT', 'SURF', 'ORB'
self.max_features = 1000
self.match_threshold = 0.7
# Tracking state
self.features = {} # {frame_number: {'keypoints': [...], 'descriptors': [...], 'positions': [...]}}
self.tracking_enabled = False
self.auto_tracking = False
# Initialize detectors
self._init_detectors()
def _init_detectors(self):
"""Initialize feature detectors based on type"""
try:
if self.detector_type == 'SIFT':
self.detector = cv2.SIFT_create(nfeatures=self.max_features)
self.matcher = cv2.BFMatcher()
elif self.detector_type == 'SURF':
self.detector = cv2.xfeatures2d.SURF_create(hessianThreshold=400)
self.matcher = cv2.BFMatcher()
elif self.detector_type == 'ORB':
self.detector = cv2.ORB_create(nfeatures=self.max_features)
self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
else:
raise ValueError(f"Unknown detector type: {self.detector_type}")
except Exception as e:
print(f"Warning: Could not initialize {self.detector_type} detector: {e}")
# Fallback to ORB
self.detector_type = 'ORB'
self.detector = cv2.ORB_create(nfeatures=self.max_features)
self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
def set_detector_type(self, detector_type: str):
"""Change detector type and reinitialize"""
if detector_type in ['SIFT', 'SURF', 'ORB']:
self.detector_type = detector_type
self._init_detectors()
print(f"Switched to {detector_type} detector")
else:
print(f"Invalid detector type: {detector_type}")
def extract_features(self, frame: np.ndarray, frame_number: int) -> bool:
"""Extract features from a frame and store them"""
try:
# Convert to grayscale if needed
if len(frame.shape) == 3:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
gray = frame
# Extract keypoints and descriptors
keypoints, descriptors = self.detector.detectAndCompute(gray, None)
if keypoints is None or descriptors is None:
return False
# Store features
self.features[frame_number] = {
'keypoints': keypoints,
'descriptors': descriptors,
'positions': [(int(kp.pt[0]), int(kp.pt[1])) for kp in keypoints]
}
print(f"Extracted {len(keypoints)} features from frame {frame_number}")
return True
except Exception as e:
print(f"Error extracting features from frame {frame_number}: {e}")
return False
def get_tracking_position(self, frame_number: int) -> Optional[Tuple[float, float]]:
"""Get the average tracking position for a frame"""
if frame_number not in self.features or not self.features[frame_number]['positions']:
return None
positions = self.features[frame_number]['positions']
if not positions:
return None
avg_x = sum(pos[0] for pos in positions) / len(positions)
avg_y = sum(pos[1] for pos in positions) / len(positions)
return (avg_x, avg_y)
def clear_features(self):
"""Clear all stored features"""
self.features.clear()
print("All features cleared")
def get_feature_count(self, frame_number: int) -> int:
"""Get number of features for a frame"""
if frame_number in self.features:
return len(self.features[frame_number]['positions'])
return 0
def serialize_features(self) -> Dict[str, Any]:
"""Serialize features for state saving"""
serialized = {}
for frame_num, frame_data in self.features.items():
frame_key = str(frame_num)
serialized[frame_key] = {
'positions': frame_data['positions'],
'keypoints': None, # Keypoints are not serialized (too large)
'descriptors': None # Descriptors are not serialized (too large)
}
return serialized
def deserialize_features(self, serialized_data: Dict[str, Any]):
"""Deserialize features from state loading"""
self.features.clear()
for frame_key, frame_data in serialized_data.items():
frame_num = int(frame_key)
self.features[frame_num] = {
'positions': frame_data['positions'],
'keypoints': None,
'descriptors': None
}
print(f"Deserialized features for {len(self.features)} frames")
def get_state_dict(self) -> Dict[str, Any]:
"""Get complete state for serialization"""
return {
'detector_type': self.detector_type,
'max_features': self.max_features,
'match_threshold': self.match_threshold,
'tracking_enabled': self.tracking_enabled,
'auto_tracking': self.auto_tracking,
'features': self.serialize_features()
}
def load_state_dict(self, state_dict: Dict[str, Any]):
"""Load complete state from serialization"""
if 'detector_type' in state_dict:
self.detector_type = state_dict['detector_type']
self._init_detectors()
if 'max_features' in state_dict:
self.max_features = state_dict['max_features']
if 'match_threshold' in state_dict:
self.match_threshold = state_dict['match_threshold']
if 'tracking_enabled' in state_dict:
self.tracking_enabled = state_dict['tracking_enabled']
if 'auto_tracking' in state_dict:
self.auto_tracking = state_dict['auto_tracking']
if 'features' in state_dict:
self.deserialize_features(state_dict['features'])
print("Feature tracker state loaded")
class Cv2BufferedCap:
"""Buffered wrapper around cv2.VideoCapture that handles frame loading, seeking, and caching correctly"""
@@ -595,6 +762,9 @@ class VideoEditor:
self.tracking_points = {} # {frame_number: [(x, y), ...]} in original frame coords
self.tracking_enabled = False
# Feature tracking system
self.feature_tracker = FeatureTracker()
# Project view mode
self.project_view_mode = False
self.project_view = None
@@ -639,7 +809,8 @@ class VideoEditor:
'seek_multiplier': getattr(self, 'seek_multiplier', 1.0),
'is_playing': getattr(self, 'is_playing', False),
'tracking_enabled': self.tracking_enabled,
'tracking_points': {str(k): v for k, v in self.tracking_points.items()}
'tracking_points': {str(k): v for k, v in self.tracking_points.items()},
'feature_tracker': self.feature_tracker.get_state_dict()
}
with open(state_file, 'w') as f:
@@ -722,6 +893,11 @@ class VideoEditor:
self.tracking_points = {int(k): v for k, v in state['tracking_points'].items()}
print(f"Loaded tracking_points: {sum(len(v) for v in self.tracking_points.values())} points")
# Load feature tracker state
if 'feature_tracker' in state:
self.feature_tracker.load_state_dict(state['feature_tracker'])
print(f"Loaded feature tracker state")
# Validate cut markers against current video length
if self.cut_start_frame is not None and self.cut_start_frame >= self.total_frames:
print(f"DEBUG: cut_start_frame {self.cut_start_frame} is beyond video length {self.total_frames}, clearing")
@@ -1055,6 +1231,18 @@ class VideoEditor:
self.current_frame = max(0, min(frame_number, self.total_frames - 1))
self.load_current_frame()
# Auto-extract features if feature tracking is enabled and auto-tracking is on
if (not self.is_image_mode and
self.feature_tracker.tracking_enabled and
self.feature_tracker.auto_tracking and
self.current_display_frame is not None):
# Only extract if we don't already have features for this frame
if self.current_frame not in self.feature_tracker.features:
# Extract features from the original frame (before transformations)
# This ensures features are in the original coordinate system
self.feature_tracker.extract_features(self.current_display_frame, self.current_frame)
def jump_to_previous_marker(self):
"""Jump to the previous tracking marker (frame with tracking points)."""
if self.is_image_mode:
@@ -1270,6 +1458,14 @@ class VideoEditor:
def _get_interpolated_tracking_position(self, frame_number):
"""Linear interpolation in ROTATED frame coords. Returns (rx, ry) or None."""
# First try feature tracking if enabled
if self.feature_tracker.tracking_enabled:
feature_pos = self.feature_tracker.get_tracking_position(frame_number)
if feature_pos:
# Features are extracted from original frames, so coordinates are already correct
return feature_pos
# Fall back to manual tracking points
if not self.tracking_points:
return None
frames = sorted(self.tracking_points.keys())
@@ -1940,13 +2136,19 @@ class VideoEditor:
motion_text = (
f" | Motion: {self.tracking_enabled}" if self.tracking_enabled else ""
)
feature_text = (
f" | Features: {self.feature_tracker.tracking_enabled}" if self.feature_tracker.tracking_enabled else ""
)
if self.feature_tracker.tracking_enabled and self.current_frame in self.feature_tracker.features:
feature_count = self.feature_tracker.get_feature_count(self.current_frame)
feature_text = f" | Features: {feature_count} pts"
autorepeat_text = (
f" | Loop: ON" if self.looping_between_markers else ""
)
if self.is_image_mode:
info_text = f"Image | Zoom: {self.zoom_factor:.1f}x{rotation_text}{brightness_text}{contrast_text}{motion_text}"
info_text = f"Image | Zoom: {self.zoom_factor:.1f}x{rotation_text}{brightness_text}{contrast_text}{motion_text}{feature_text}"
else:
info_text = f"Frame: {self.current_frame}/{self.total_frames} | Speed: {self.playback_speed:.1f}x | Zoom: {self.zoom_factor:.1f}x{seek_multiplier_text}{rotation_text}{brightness_text}{contrast_text}{motion_text}{autorepeat_text} | {'Playing' if self.is_playing else 'Paused'}"
info_text = f"Frame: {self.current_frame}/{self.total_frames} | Speed: {self.playback_speed:.1f}x | Zoom: {self.zoom_factor:.1f}x{seek_multiplier_text}{rotation_text}{brightness_text}{contrast_text}{motion_text}{feature_text}{autorepeat_text} | {'Playing' if self.is_playing else 'Paused'}"
cv2.putText(
canvas,
info_text,
@@ -2039,6 +2241,16 @@ class VideoEditor:
cv2.circle(canvas, (sx, sy), 6, (255, 0, 0), -1)
cv2.circle(canvas, (sx, sy), 6, (255, 255, 255), 1)
# Draw feature tracking points (green circles)
if (not self.is_image_mode and
self.feature_tracker.tracking_enabled and
self.current_frame in self.feature_tracker.features):
feature_positions = self.feature_tracker.features[self.current_frame]['positions']
for (fx, fy) in feature_positions:
sx, sy = self._map_rotated_to_screen(fx, fy)
cv2.circle(canvas, (sx, sy), 4, (0, 255, 0), -1) # Green circles for features
cv2.circle(canvas, (sx, sy), 4, (255, 255, 255), 1)
# Draw previous and next tracking points with motion path visualization
if not self.is_image_mode and self.tracking_points:
prev_result = self._get_previous_tracking_point()
@@ -3273,6 +3485,47 @@ class VideoEditor:
self.tracking_points = {}
self.show_feedback_message("Tracking points cleared")
self.save_state()
elif key == ord("f"):
# Toggle feature tracking on/off
self.feature_tracker.tracking_enabled = not self.feature_tracker.tracking_enabled
self.show_feedback_message(f"Feature tracking {'ON' if self.feature_tracker.tracking_enabled else 'OFF'}")
self.save_state()
elif key == ord("F"):
# Extract features from current frame
if not self.is_image_mode and self.current_display_frame is not None:
# Extract features from the original frame (before transformations)
# This ensures features are in the original coordinate system
success = self.feature_tracker.extract_features(self.current_display_frame, self.current_frame)
if success:
count = self.feature_tracker.get_feature_count(self.current_frame)
self.show_feedback_message(f"Extracted {count} features from frame {self.current_frame}")
else:
self.show_feedback_message("Failed to extract features")
self.save_state()
else:
self.show_feedback_message("No frame data available")
elif key == ord("g"):
# Toggle auto tracking
self.feature_tracker.auto_tracking = not self.feature_tracker.auto_tracking
self.show_feedback_message(f"Auto tracking {'ON' if self.feature_tracker.auto_tracking else 'OFF'}")
self.save_state()
elif key == ord("G"):
# Clear all feature tracking data
self.feature_tracker.clear_features()
self.show_feedback_message("Feature tracking data cleared")
self.save_state()
elif key == ord("h"):
# Switch detector type (SIFT -> SURF -> ORB -> SIFT)
current_type = self.feature_tracker.detector_type
if current_type == 'SIFT':
new_type = 'SURF'
elif current_type == 'SURF':
new_type = 'ORB'
else:
new_type = 'SIFT'
self.feature_tracker.set_detector_type(new_type)
self.show_feedback_message(f"Detector switched to {new_type}")
self.save_state()
elif key == ord("t"):
# Marker looping only for videos
if not self.is_image_mode:

57
uv.lock generated
View File

@@ -15,12 +15,14 @@ source = { virtual = "croppa" }
dependencies = [
{ name = "numpy" },
{ name = "opencv-python" },
{ name = "pillow" },
]
[package.metadata]
requires-dist = [
{ name = "numpy", specifier = ">=1.24.0" },
{ name = "opencv-python", specifier = ">=4.8.0" },
{ name = "pillow", specifier = ">=10.0.0" },
]
[[package]]
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]
[[package]]
name = "pillow"
version = "11.3.0"
source = { registry = "https://pypi.org/simple" }
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