Implement toggle for the keyframes, disabled by default

Refine the crop border dragging logic by introducing mouse button state tracking and adjusting the crop dimensions based on drag direction. The minimum drag distance for crop adjustments has been reduced to improve responsiveness, allowing for more intuitive user interactions when resizing the crop area. This update also clarifies the drag mode for expanding or contracting the crop edges.

.gitignore

@@ -1,3 +1,5 @@
 __pycache__
 croppa/build/lib
 croppa/croppa.egg-info
+*.log
+*.mp4

cleaner/go.mod

@@ -0,0 +1,14 @@
+module tcleaner
+
+go 1.23.6
+
+require git.site.quack-lab.dev/dave/cylogger v1.4.0
+
+require (
+	github.com/google/go-cmp v0.5.9 // indirect
+	github.com/hexops/valast v1.5.0 // indirect
+	golang.org/x/mod v0.7.0 // indirect
+	golang.org/x/sys v0.3.0 // indirect
+	golang.org/x/tools v0.4.0 // indirect
+	mvdan.cc/gofumpt v0.4.0 // indirect
+)

cleaner/go.sum

@@ -0,0 +1,28 @@
+git.site.quack-lab.dev/dave/cylogger v1.4.0 h1:3Ca7V5JWvruARJd5S8xDFwW9LnZ9QInqkYLRdrEFvuY=
+git.site.quack-lab.dev/dave/cylogger v1.4.0/go.mod h1:wctgZplMvroA4X6p8f4B/LaCKtiBcT1Pp+L14kcS8jk=
+github.com/frankban/quicktest v1.14.3 h1:FJKSZTDHjyhriyC81FLQ0LY93eSai0ZyR/ZIkd3ZUKE=
+github.com/frankban/quicktest v1.14.3/go.mod h1:mgiwOwqx65TmIk1wJ6Q7wvnVMocbUorkibMOrVTHZps=
+github.com/google/go-cmp v0.5.9 h1:O2Tfq5qg4qc4AmwVlvv0oLiVAGB7enBSJ2x2DqQFi38=
+github.com/google/go-cmp v0.5.9/go.mod h1:17dUlkBOakJ0+DkrSSNjCkIjxS6bF9zb3elmeNGIjoY=
+github.com/hexops/autogold v0.8.1 h1:wvyd/bAJ+Dy+DcE09BoLk6r4Fa5R5W+O+GUzmR985WM=
+github.com/hexops/autogold v0.8.1/go.mod h1:97HLDXyG23akzAoRYJh/2OBs3kd80eHyKPvZw0S5ZBY=
+github.com/hexops/gotextdiff v1.0.3 h1:gitA9+qJrrTCsiCl7+kh75nPqQt1cx4ZkudSTLoUqJM=
+github.com/hexops/gotextdiff v1.0.3/go.mod h1:pSWU5MAI3yDq+fZBTazCSJysOMbxWL1BSow5/V2vxeg=
+github.com/hexops/valast v1.5.0 h1:FBTuvVi0wjTngtXJRZXMbkN/Dn6DgsUsBwch2DUJU8Y=
+github.com/hexops/valast v1.5.0/go.mod h1:Jcy1pNH7LNraVaAZDLyv21hHg2WBv9Nf9FL6fGxU7o4=
+github.com/kr/pretty v0.3.0 h1:WgNl7dwNpEZ6jJ9k1snq4pZsg7DOEN8hP9Xw0Tsjwk0=
+github.com/kr/pretty v0.3.0/go.mod h1:640gp4NfQd8pI5XOwp5fnNeVWj67G7CFk/SaSQn7NBk=
+github.com/kr/text v0.2.0 h1:5Nx0Ya0ZqY2ygV366QzturHI13Jq95ApcVaJBhpS+AY=
+github.com/kr/text v0.2.0/go.mod h1:eLer722TekiGuMkidMxC/pM04lWEeraHUUmBw8l2grE=
+github.com/rogpeppe/go-internal v1.9.0 h1:73kH8U+JUqXU8lRuOHeVHaa/SZPifC7BkcraZVejAe8=
+github.com/rogpeppe/go-internal v1.9.0/go.mod h1:WtVeX8xhTBvf0smdhujwtBcq4Qrzq/fJaraNFVN+nFs=
+golang.org/x/mod v0.7.0 h1:LapD9S96VoQRhi/GrNTqeBJFrUjs5UHCAtTlgwA5oZA=
+golang.org/x/mod v0.7.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
+golang.org/x/sync v0.1.0 h1:wsuoTGHzEhffawBOhz5CYhcrV4IdKZbEyZjBMuTp12o=
+golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
+golang.org/x/sys v0.3.0 h1:w8ZOecv6NaNa/zC8944JTU3vz4u6Lagfk4RPQxv92NQ=
+golang.org/x/sys v0.3.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/tools v0.4.0 h1:7mTAgkunk3fr4GAloyyCasadO6h9zSsQZbwvcaIciV4=
+golang.org/x/tools v0.4.0/go.mod h1:UE5sM2OK9E/d67R0ANs2xJizIymRP5gJU295PvKXxjQ=
+mvdan.cc/gofumpt v0.4.0 h1:JVf4NN1mIpHogBj7ABpgOyZc65/UUOkKQFkoURsz4MM=
+mvdan.cc/gofumpt v0.4.0/go.mod h1:PljLOHDeZqgS8opHRKLzp2It2VBuSdteAgqUfzMTxlQ=

cleaner/install_grader_context_menu.reg

@@ -0,0 +1,7 @@
+Windows Registry Editor Version 5.00
+
+[HKEY_CURRENT_USER\Software\Classes\*\shell\Clean name]
+@="Clean name"
+
+[HKEY_CURRENT_USER\Software\Classes\*\shell\Clean name\command]
+@="C:\\Users\\administrator\\go\\bin\\tcleaner.exe \"%1\"" 

cleaner/main.go

@@ -0,0 +1,76 @@
+package main
+
+import (
+	"flag"
+	"fmt"
+	"os"
+	"path/filepath"
+	"regexp"
+	logger "git.site.quack-lab.dev/dave/cylogger"
+)
+
+func main() {
+	flag.Parse()
+	logger.InitFlag()
+	if flag.NArg() == 0 {
+		fmt.Println("Usage: cleaner <files>")
+		os.Exit(1)
+	}
+	// regex to match "2025-07-08"
+	re := regexp.MustCompile(`\d{4}-\d{2}-\d{2}`)
+	editedRe := regexp.MustCompile(`_edited_\d{5}`)
+
+	for _, file := range flag.Args() {
+		filelog := logger.Default.WithPrefix(file)
+		filelog.Info("Processing file")
+
+		info, err := os.Stat(file)
+		if err != nil {
+			filelog.Error("ERROR: %v\n", err)
+			continue
+		}
+		if info.IsDir() {
+			filelog.Info("SKIP (directory): %s\n", file)
+			continue
+		}
+
+		name := filepath.Base(file)
+		match := re.FindStringSubmatch(name)
+		filelog.Debug("Match: %v", match)
+		if match == nil {
+			filelog.Info("SKIP (no date pattern): %s\n", name)
+			continue
+		}
+		
+		namePart := match[0]
+		editMatch := editedRe.FindStringSubmatch(name)
+		filelog.Debug("Edit match: %v", editMatch)
+		if editMatch != nil {
+			namePart = namePart + editMatch[0]
+			filelog.Info("Video has edited part, new name: %s", namePart)
+		}
+
+		newName := namePart + filepath.Ext(name)
+		filelog.Debug("New name: %s", newName)
+		if name == newName {
+			filelog.Info("SKIP (already named): %s\n", name)
+			continue
+		}
+
+		filelog.Debug("Checking if target exists: %s", newName)
+		if _, err := os.Stat(newName); err == nil {
+			filelog.Info("SKIP (target exists): %s -> %s\n", name, newName)
+			continue
+		}
+
+		filelog.Info("Renaming to: %s", newName)
+		err = os.Rename(name, newName)
+		if err != nil {
+			filelog.Error("ERROR renaming %s: %v\n", name, err)
+		} else {
+			filelog.Info("RENAMED: %s -> %s\n", name, newName)
+		}
+		filelog.Info("All done")
+		continue
+	}
+}

croppa/capture.py

@@ -0,0 +1,68 @@
+import cv2
+from collections import OrderedDict
+
+
+class Cv2BufferedCap:
+    """Buffered wrapper around cv2.VideoCapture that handles frame loading, seeking, and caching correctly"""
+    
+    def __init__(self, video_path, backend=None, cache_size=10000):
+        self.video_path = video_path
+        self.cap = cv2.VideoCapture(str(video_path), backend)
+        if not self.cap.isOpened():
+            raise ValueError(f"Could not open video: {video_path}")
+        
+        # Video properties
+        self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        self.fps = self.cap.get(cv2.CAP_PROP_FPS)
+        self.frame_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        self.frame_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        # Current position tracking
+        self.current_frame = 0
+        
+        # Frame cache (LRU)
+        self.cache_size = cache_size
+        self.frame_cache = OrderedDict()
+    
+    def get_frame(self, frame_number):
+        """Get frame at specific index - always accurate"""
+        # Clamp frame number to valid range
+        frame_number = max(0, min(frame_number, self.total_frames - 1))
+        
+        # Check cache first
+        if frame_number in self.frame_cache:
+            self.frame_cache.move_to_end(frame_number)
+            return self.frame_cache[frame_number]
+        
+        # Optimize for sequential reading (next frame)
+        if frame_number == self.current_frame + 1:
+            ret, frame = self.cap.read()
+        else:
+            # Seek for non-sequential access
+            self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
+            ret, frame = self.cap.read()
+        
+        if ret:
+            self.current_frame = frame_number
+            # Store in cache, evict least recently used if cache is full
+            if len(self.frame_cache) >= self.cache_size:
+                self.frame_cache.popitem(last=False)
+            self.frame_cache[frame_number] = frame
+            self.frame_cache.move_to_end(frame_number)
+            return frame
+        else:
+            raise ValueError(f"Failed to read frame {frame_number}")
+    
+    def advance_frame(self, frames=1):
+        """Advance by specified number of frames"""
+        new_frame = self.current_frame + frames
+        return self.get_frame(new_frame)
+    
+    def release(self):
+        """Release the video capture"""
+        if self.cap:
+            self.cap.release()
+    
+    def isOpened(self):
+        """Check if capture is opened"""
+        return self.cap and self.cap.isOpened()

croppa/project_view.py

@@ -0,0 +1,351 @@
+import cv2
+import json
+import numpy as np
+from pathlib import Path
+
+
+class ProjectView:
+    """Project view that displays videos in current directory with progress bars"""
+    
+    # Project view configuration
+    THUMBNAIL_SIZE = (200, 150)  # Width, Height
+    THUMBNAIL_MARGIN = 20
+    PROGRESS_BAR_HEIGHT = 8
+    TEXT_HEIGHT = 30
+    
+    # Colors
+    BG_COLOR = (40, 40, 40)
+    THUMBNAIL_BG_COLOR = (60, 60, 60)
+    PROGRESS_BG_COLOR = (80, 80, 80)
+    PROGRESS_FILL_COLOR = (0, 120, 255)
+    TEXT_COLOR = (255, 255, 255)
+    SELECTED_COLOR = (255, 165, 0)
+    
+    def __init__(self, directory: Path, video_editor):
+        self.directory = directory
+        self.video_editor = video_editor
+        self.video_files = []
+        self.thumbnails = {}
+        self.progress_data = {}
+        self.selected_index = 0
+        self.scroll_offset = 0
+        self.items_per_row = 2  # Default to 2 items per row
+        self.window_width = 1920  # Increased to accommodate 1080p videos
+        self.window_height = 1200
+        
+        self._load_video_files()
+        self._load_progress_data()
+    
+    def _calculate_thumbnail_size(self, window_width: int) -> tuple:
+        """Calculate thumbnail size based on items per row and window width"""
+        available_width = window_width - self.THUMBNAIL_MARGIN
+        item_width = (available_width - (self.items_per_row - 1) * self.THUMBNAIL_MARGIN) // self.items_per_row
+        thumbnail_width = max(50, item_width)  # Minimum 50px width
+        thumbnail_height = int(thumbnail_width * self.THUMBNAIL_SIZE[1] / self.THUMBNAIL_SIZE[0])  # Maintain aspect ratio
+        return (thumbnail_width, thumbnail_height)
+    
+    def _load_video_files(self):
+        """Load all video files from directory"""
+        self.video_files = []
+        for file_path in self.directory.iterdir():
+            if (file_path.is_file() and 
+                file_path.suffix.lower() in self.video_editor.VIDEO_EXTENSIONS):
+                self.video_files.append(file_path)
+        self.video_files.sort(key=lambda x: x.name)
+    
+    def _load_progress_data(self):
+        """Load progress data from JSON state files"""
+        self.progress_data = {}
+        for video_path in self.video_files:
+            state_file = video_path.with_suffix('.json')
+            if state_file.exists():
+                try:
+                    with open(state_file, 'r') as f:
+                        state = json.load(f)
+                    current_frame = state.get('current_frame', 0)
+                    
+                    # Get total frames from video
+                    cap = cv2.VideoCapture(str(video_path))
+                    if cap.isOpened():
+                        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+                        cap.release()
+                        
+                        if total_frames > 0:
+                            progress = current_frame / (total_frames - 1)
+                            self.progress_data[video_path] = {
+                                'current_frame': current_frame,
+                                'total_frames': total_frames,
+                                'progress': progress
+                            }
+                except Exception as e:
+                    print(f"Error loading progress for {video_path.name}: {e}")
+    
+    def refresh_progress_data(self):
+        """Refresh progress data from JSON files (call when editor state changes)"""
+        self._load_progress_data()
+    
+    def get_progress_for_video(self, video_path: Path) -> float:
+        """Get progress (0.0 to 1.0) for a video"""
+        if video_path in self.progress_data:
+            return self.progress_data[video_path]['progress']
+        return 0.0
+    
+    def get_thumbnail_for_video(self, video_path: Path, size: tuple = None) -> np.ndarray:
+        """Get thumbnail for a video, generating it if needed"""
+        if size is None:
+            size = self.THUMBNAIL_SIZE
+        
+        # Cache the original thumbnail by video path only (not size)
+        if video_path in self.thumbnails:
+            original_thumbnail = self.thumbnails[video_path]
+            # Resize the cached thumbnail to the requested size
+            return cv2.resize(original_thumbnail, size)
+        
+        # Generate original thumbnail on demand (only once per video)
+        try:
+            cap = cv2.VideoCapture(str(video_path))
+            if cap.isOpened():
+                total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+                if total_frames > 0:
+                    middle_frame = total_frames // 2
+                    cap.set(cv2.CAP_PROP_POS_FRAMES, middle_frame)
+                    ret, frame = cap.read()
+                    if ret:
+                        # Store original thumbnail at original size
+                        original_thumbnail = cv2.resize(frame, self.THUMBNAIL_SIZE)
+                        self.thumbnails[video_path] = original_thumbnail
+                        cap.release()
+                        # Return resized version
+                        return cv2.resize(original_thumbnail, size)
+            cap.release()
+        except Exception as e:
+            print(f"Error generating thumbnail for {video_path.name}: {e}")
+        
+        # Return a placeholder if thumbnail generation failed
+        placeholder = np.full((size[1], size[0], 3), 
+                            self.THUMBNAIL_BG_COLOR, dtype=np.uint8)
+        return placeholder
+    
+    def draw(self) -> np.ndarray:
+        """Draw the project view"""
+        # Get actual window size dynamically
+        try:
+            # Try to get the actual window size from OpenCV
+            window_rect = cv2.getWindowImageRect("Project View")
+            if window_rect[2] > 0 and window_rect[3] > 0:  # width and height > 0
+                actual_width = window_rect[2]
+                actual_height = window_rect[3]
+            else:
+                # Fallback to default size
+                actual_width = self.window_width
+                actual_height = self.window_height
+        except:
+            # Fallback to default size
+            actual_width = self.window_width
+            actual_height = self.window_height
+        
+        canvas = np.full((actual_height, actual_width, 3), self.BG_COLOR, dtype=np.uint8)
+        
+        if not self.video_files:
+            # No videos message
+            text = "No videos found in directory"
+            font = cv2.FONT_HERSHEY_SIMPLEX
+            text_size = cv2.getTextSize(text, font, 1.0, 2)[0]
+            text_x = (actual_width - text_size[0]) // 2
+            text_y = (actual_height - text_size[1]) // 2
+            cv2.putText(canvas, text, (text_x, text_y), font, 1.0, self.TEXT_COLOR, 2)
+            return canvas
+        
+        # Calculate layout - use fixed items_per_row and calculate thumbnail size to fit
+        items_per_row = min(self.items_per_row, len(self.video_files))  # Don't exceed number of videos
+        
+        # Calculate thumbnail size to fit the desired number of items per row
+        thumbnail_width, thumbnail_height = self._calculate_thumbnail_size(actual_width)
+        
+        # Calculate item height dynamically based on thumbnail size
+        item_height = thumbnail_height + self.PROGRESS_BAR_HEIGHT + self.TEXT_HEIGHT + self.THUMBNAIL_MARGIN
+        
+        item_width = (actual_width - (items_per_row + 1) * self.THUMBNAIL_MARGIN) // items_per_row
+        
+        # Draw videos in grid
+        for i, video_path in enumerate(self.video_files):
+            row = i // items_per_row
+            col = i % items_per_row
+            
+            # Skip if scrolled out of view
+            if row < self.scroll_offset:
+                continue
+            if row > self.scroll_offset + (actual_height // item_height):
+                break
+            
+            # Calculate position
+            x = self.THUMBNAIL_MARGIN + col * (item_width + self.THUMBNAIL_MARGIN)
+            y = self.THUMBNAIL_MARGIN + (row - self.scroll_offset) * item_height
+            
+            # Draw thumbnail background
+            cv2.rectangle(canvas, 
+                         (x, y), 
+                         (x + thumbnail_width, y + thumbnail_height), 
+                         self.THUMBNAIL_BG_COLOR, -1)
+            
+            # Draw selection highlight
+            if i == self.selected_index:
+                cv2.rectangle(canvas, 
+                             (x - 2, y - 2), 
+                             (x + thumbnail_width + 2, y + thumbnail_height + 2), 
+                             self.SELECTED_COLOR, 3)
+            
+            # Draw thumbnail
+            thumbnail = self.get_thumbnail_for_video(video_path, (thumbnail_width, thumbnail_height))
+            # Thumbnail is already the correct size, no need to resize
+            resized_thumbnail = thumbnail
+            
+            # Ensure thumbnail doesn't exceed canvas bounds
+            end_y = min(y + thumbnail_height, actual_height)
+            end_x = min(x + thumbnail_width, actual_width)
+            thumb_height = end_y - y
+            thumb_width = end_x - x
+            
+            if thumb_height > 0 and thumb_width > 0:
+                # Resize thumbnail to fit within bounds if necessary
+                if thumb_height != thumbnail_height or thumb_width != thumbnail_width:
+                    resized_thumbnail = cv2.resize(thumbnail, (thumb_width, thumb_height))
+                
+                canvas[y:end_y, x:end_x] = resized_thumbnail
+            
+            # Draw progress bar
+            progress_y = y + thumbnail_height + 5
+            progress_width = thumbnail_width
+            progress = self.get_progress_for_video(video_path)
+            
+            # Progress background
+            cv2.rectangle(canvas, 
+                         (x, progress_y), 
+                         (x + progress_width, progress_y + self.PROGRESS_BAR_HEIGHT), 
+                         self.PROGRESS_BG_COLOR, -1)
+            
+            # Progress fill
+            if progress > 0:
+                fill_width = int(progress_width * progress)
+                cv2.rectangle(canvas, 
+                             (x, progress_y), 
+                             (x + fill_width, progress_y + self.PROGRESS_BAR_HEIGHT), 
+                             self.PROGRESS_FILL_COLOR, -1)
+            
+            # Draw filename
+            filename = video_path.name
+            # Truncate if too long
+            if len(filename) > 25:
+                filename = filename[:22] + "..."
+            
+            text_y = progress_y + self.PROGRESS_BAR_HEIGHT + 20
+            cv2.putText(canvas, filename, (x, text_y), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, self.TEXT_COLOR, 2)
+            
+            # Draw progress percentage
+            if video_path in self.progress_data:
+                progress_text = f"{progress * 100:.0f}%"
+                text_size = cv2.getTextSize(progress_text, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)[0]
+                progress_text_x = x + progress_width - text_size[0]
+                cv2.putText(canvas, progress_text, (progress_text_x, text_y), 
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.4, self.TEXT_COLOR, 1)
+        
+        # Draw instructions
+        instructions = [
+            "Project View - Videos in current directory",
+            "WASD: Navigate | E: Open video | Q: Fewer items per row | Y: More items per row | q: Quit | ESC: Back to editor",
+            f"Showing {len(self.video_files)} videos | {items_per_row} per row | Thumbnail: {thumbnail_width}x{thumbnail_height}"
+        ]
+        
+        for i, instruction in enumerate(instructions):
+            y_pos = actual_height - 60 + i * 20
+            cv2.putText(canvas, instruction, (10, y_pos), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, self.TEXT_COLOR, 1)
+        
+        return canvas
+    
+    def handle_key(self, key: int) -> str:
+        """Handle keyboard input, returns action taken"""
+        if key == 27:  # ESC
+            return "back_to_editor"
+        elif key == ord('q'):  # lowercase q - Quit
+            return "quit"
+        elif key == ord('e') or key == ord('E'):  # E - Open video
+            if self.video_files and 0 <= self.selected_index < len(self.video_files):
+                return f"open_video:{self.video_files[self.selected_index]}"
+        elif key == ord('w') or key == ord('W'):  # W - Up
+            current_items_per_row = min(self.items_per_row, len(self.video_files))
+            if self.selected_index >= current_items_per_row:
+                self.selected_index -= current_items_per_row
+            else:
+                self.selected_index = 0
+            self._update_scroll()
+        elif key == ord('s') or key == ord('S'):  # S - Down
+            current_items_per_row = min(self.items_per_row, len(self.video_files))
+            if self.selected_index + current_items_per_row < len(self.video_files):
+                self.selected_index += current_items_per_row
+            else:
+                self.selected_index = len(self.video_files) - 1
+            self._update_scroll()
+        elif key == ord('a') or key == ord('A'):  # A - Left
+            if self.selected_index > 0:
+                self.selected_index -= 1
+            self._update_scroll()
+        elif key == ord('d') or key == ord('D'):  # D - Right
+            if self.selected_index < len(self.video_files) - 1:
+                self.selected_index += 1
+            self._update_scroll()
+        elif key == ord('Q'):  # uppercase Q - Fewer items per row (larger thumbnails)
+            if self.items_per_row > 1:
+                self.items_per_row -= 1
+            print(f"Items per row: {self.items_per_row}")
+        elif key == ord('y') or key == ord('Y'):  # Y - More items per row (smaller thumbnails)
+            self.items_per_row += 1
+            print(f"Items per row: {self.items_per_row}")
+        
+        return "none"
+    
+    def _update_scroll(self):
+        """Update scroll offset based on selected item"""
+        if not self.video_files:
+            return
+        
+        # Use fixed items per row
+        items_per_row = min(self.items_per_row, len(self.video_files))
+        
+        # Get window dimensions for calculations
+        try:
+            window_rect = cv2.getWindowImageRect("Project View")
+            if window_rect[2] > 0 and window_rect[3] > 0:
+                window_width = window_rect[2]
+                window_height = window_rect[3]
+            else:
+                window_width = self.window_width
+                window_height = self.window_height
+        except:
+            window_width = self.window_width
+            window_height = self.window_height
+        
+        # Calculate thumbnail size and item height dynamically
+        thumbnail_width, thumbnail_height = self._calculate_thumbnail_size(window_width)
+        item_height = thumbnail_height + self.PROGRESS_BAR_HEIGHT + self.TEXT_HEIGHT + self.THUMBNAIL_MARGIN
+        
+        selected_row = self.selected_index // items_per_row
+        visible_rows = max(1, window_height // item_height)
+        
+        # Calculate how many rows we can actually show
+        total_rows = (len(self.video_files) + items_per_row - 1) // items_per_row
+        
+        # If we can show all rows, no scrolling needed
+        if total_rows <= visible_rows:
+            self.scroll_offset = 0
+            return
+        
+        # Update scroll to keep selected item visible
+        if selected_row < self.scroll_offset:
+            self.scroll_offset = selected_row
+        elif selected_row >= self.scroll_offset + visible_rows:
+            self.scroll_offset = selected_row - visible_rows + 1
+        
+        # Ensure scroll offset doesn't go negative or beyond available content
+        self.scroll_offset = max(0, min(self.scroll_offset, total_rows - visible_rows))

croppa/pyproject.toml

@@ -2,12 +2,15 @@
 name = "croppa"
 version = "0.1.0"
 description = "Fast and lightweight video editor for cropping, zooming, and cutting"
-readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
     "opencv-python>=4.8.0",
-    "numpy>=1.24.0"
+    "numpy>=1.24.0",
+    "Pillow>=10.0.0"
 ]
 
 [project.scripts]
 croppa = "main:main"
+
+[tool.setuptools]
+py-modules = ["capture", "main", "project_view", "tracking", "utils"]

croppa/spec.md

@@ -0,0 +1,138 @@
+# Croppa - Feature Specification
+
+## Overview
+Croppa is a lightweight video and image editor that provides real-time editing capabilities with persistent state management.
+
+## Notes:
+
+Note the distinction between lowercase and uppercase keys
+Uppercase keys imply shift+key
+
+Note that every transformation (cropping, motion track points) are almost always applied to an already transformed frame
+Be that by rotation, cropping, zooming or motion tracking itself
+Which means that the user input must be "de-transformed" before being applied to the frame
+In other words if we zoom into an area and right click to add a tracking point it must be added to that exact pixel ON THE ORIGINAL FRAME
+And NOT the zoomed in / processed frame
+Likwise with rotations
+All coordinates (crop region, zoom center, motion tracking point) are to be in reference to the original raw unprocessed frame
+To then display these points they must be transformed to the processed - display - frame
+Likewise when accepting user input from the processed display frame the coordinates must be transformed back to the original raw unprocessed frame
+A simple example if we are rotated by 90 degrees and click on the top left corner of the display frame
+That coordinate is to be mapped to the bottom left corner of the original raw unprocessed frame
+
+The input to the editor is either a list of video files
+Or a directory
+In the case a directory is provided the editor is to open "all" editable files in the given directory
+In the case multiple files are open we are able to navigate between them using n and N keys for next and previous file
+Be careful to save and load settings when navigating this way
+
+## Core Features
+
+### Video Playback
+- **Space**: Play/pause video
+- **a/d**: Seek backward/forward 1 frame
+- **A/D**: Seek backward/forward 10 frames  
+- **Ctrl+a/d**: Seek backward/forward 60 frames
+- **Mouse Wheel**: Seek backward/forward 1 frame (ignores seek multiplier)
+- **W/S**: Increase/decrease playback speed (0.1x to 10.0x, increments of 0.2)
+- **Q/Y**: Increase/decrease seek multiplier (multiplies the frame count for a/d/A/D/Ctrl+a/d keys by 1.0x to 100.0x, increments of 2.0)
+- **q**: Quit the program
+- **Timeline**: Click anywhere to jump to that position
+- **Auto-repeat**: Hold seek keys for continuous seeking at 1 FPS rate
+
+### Visual Transformations
+- **-**: Rotate 90 degrees clockwise
+- **e/E**: Increase/decrease brightness (-100 to +100, increments of 5)
+- **r/R**: Increase/decrease contrast (0.1 to 3.0, increments of 0.1)
+- **Ctrl+Scroll**: Zoom in/out (0.1x to 10.0x, increments of 0.1)
+- **Ctrl+Click**: Set zoom center point
+
+### Cropping
+- **Shift+Click+Drag**: Select crop area with green rectangle preview
+- **h/j/k/l**: Expand crop from right/down/up/left edges (15 pixels per keypress)
+- **H/J/K/L**: Contract crop to left/down/up/right edges (15 pixels per keypress)
+- **u**: Undo last crop
+- **c**: Clear all cropping
+- **C**: Complete reset (crop, zoom, rotation, brightness, contrast, tracking points, cut markers)
+
+### Motion Tracking
+- **Right-click**: Add tracking point (green circle with white border)
+- **Right-click existing point**: Remove tracking point (within 10px)
+- **Right-click near existing point**: Snap to existing point from any frame (within 10px radius)
+- **Right-click near motion path**: Snap to closest point on yellow arrow line between tracking points (within 10px radius)
+- **v**: Toggle motion tracking on/off
+- **V**: Clear all tracking points
+- **Blue cross**: Shows computed tracking position
+- **Automatic interpolation**: Tracks between keyframes
+- **Crop follows**: Crop area centers on tracked object
+- **Display** Points are rendered as blue dots per frame, in addition the previous tracking point (red) and next tracking point (magenta) are shown with yellow arrows indicating motion direction
+
+#### Motion Tracking Navigation
+- **,**: Jump to previous tracking marker (previous frame that has one or more tracking points). Goes to first marker if at beginning.
+- **.**: Jump to next tracking marker (next frame that has one or more tracking points). Goes to last marker if at end.
+
+### Markers and Looping
+- **1**: Set cut start marker at current frame
+- **2**: Set cut end marker at current frame
+- **t**: Toggle loop playback between markers
+- **Red lines**: Markers shown on timeline with numbers
+- **Continuous loop**: Playback loops between markers when enabled
+
+### File Management
+- **Enter**: Render video (overwrites if filename contains "_edited_")
+- **b**: Render video with new "_edited_001" filename (does NOT overwrite!)
+- **s**: Save screenshot with auto-incrementing filename (video_frame_00001.jpg, video_frame_00002.jpg, etc. - NEVER overwrite existing screenshots)
+- **N/n**: Next/previous video in directory
+- **p**: Toggle project view (directory browser)
+
+### Project View
+- **wasd**: Navigate through video thumbnails
+- **e**: Open selected video
+- **Q/Y**: Change thumbnail size (fewer/more per row, size automatically computed to fit row)
+- **q**: Quit
+- **Progress bars**: Show editing progress for each video (blue bar showing current_frame/total_frames)
+- **ESC**: Return to editor
+
+### Display and Interface
+- **f**: Toggle fullscreen
+- **Status overlay**: Shows "Frame: 1500/3000 | Speed: 1.5x | Zoom: 2.0x | Seek: 5.0x | Rotation: 90° | Brightness: 10 | Contrast: 1.2 | Motion: ON (3 pts) | Playing/Paused"
+- **Timeline**: Visual progress bar with current position handle
+- **Feedback messages**: Temporary on-screen notifications (e.g. "Screenshot saved: video_frame_00001.jpg")
+- **Progress bar**: Shows rendering progress with FPS counter (e.g. "Processing 1500/3000 frames | 25.3 FPS")
+
+### State Management
+- **Auto-save**: Settings saved automatically on changes and on quit
+- **Per-video state**: Each video remembers its own settings
+- **Cross-session**: Settings persist between application restarts
+- **JSON files**: State stored as .json files next to videos with the same name as the video
+
+### Rendering
+- **Background rendering**: Continue editing while rendering (rendering happens in separate thread, you can still seek/play/edit)
+- **x**: Cancel active render
+- **FFmpeg output**: Invoke FFmpeg process, pipe raw video frames via stdin, output MP4 with H.264 encoding (CRF 18, preset fast)
+- **Progress tracking**: Real-time progress with FPS display
+- **Overwrite protection**: Only overwrites files with "_edited_" in name
+
+### Image Mode
+- **Same controls**: All editing features work on static images
+- **No playback**: Space key disabled, no timeline
+- **Screenshot mode**: Treats single images like video frames
+
+### Error Handling
+- **Format support**: MP4, AVI, MOV, MKV, WMV, FLV, WebM, M4V, JPG, PNG, BMP, TIFF, WebP
+- **Backend fallback**: Tries multiple video backends automatically
+- **Error messages**: Clear feedback for common issues
+- **Graceful degradation**: Continues working when possible
+
+### Performance Features
+- **Frame caching**: Smooth seeking with cached frames (cache the decoded frames, LRU eviction, max 3000 frames)
+- **Transformation caching**: Fast repeated operations (cache transformed frames during auto-repeat seeking)
+- **Memory management**: Automatic cache cleanup
+
+### Window Management
+- **Resizable**: Window can be resized dynamically
+- **Multi-window**: Project view opens in separate window
+- **Focus handling**: Keys only affect active window
+- **Context menu**: Right-click integration on Windows
+
+This specification describes what Croppa does from the user's perspective - the features, controls, and behaviors that make up the application.

croppa/tracking.py

@@ -0,0 +1,248 @@
+import cv2
+import numpy as np
+from typing import Dict, Any
+
+
+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)
+            elif self.detector_type == 'SURF':
+                # SURF requires opencv-contrib-python, fallback to SIFT
+                print("Warning: SURF requires opencv-contrib-python package. Using SIFT instead.")
+                self.detector = cv2.SIFT_create(nfeatures=self.max_features)
+                self.detector_type = 'SIFT'
+            elif self.detector_type == 'ORB':
+                self.detector = cv2.ORB_create(nfeatures=self.max_features)
+            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)
+    
+    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, coord_mapper=None) -> 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
+            
+            # Map coordinates back to original frame space if mapper provided
+            if coord_mapper:
+                mapped_positions = []
+                for kp in keypoints:
+                    orig_x, orig_y = coord_mapper(kp.pt[0], kp.pt[1])
+                    mapped_positions.append((int(orig_x), int(orig_y)))
+            else:
+                mapped_positions = [(int(kp.pt[0]), int(kp.pt[1])) for kp in keypoints]
+            
+            # Store features
+            self.features[frame_number] = {
+                'keypoints': keypoints,
+                'descriptors': descriptors,
+                'positions': mapped_positions
+            }
+            
+            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 extract_features_from_region(self, frame: np.ndarray, frame_number: int, coord_mapper=None) -> bool:
+        """Extract features from a frame and ADD them to existing features"""
+        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
+            
+            # Map coordinates back to original frame space if mapper provided
+            if coord_mapper:
+                mapped_positions = []
+                for kp in keypoints:
+                    orig_x, orig_y = coord_mapper(kp.pt[0], kp.pt[1])
+                    mapped_positions.append((int(orig_x), int(orig_y)))
+            else:
+                mapped_positions = [(int(kp.pt[0]), int(kp.pt[1])) for kp in keypoints]
+            
+            # Add to existing features or create new entry
+            if frame_number in self.features:
+                # Check if descriptor dimensions match
+                existing_features = self.features[frame_number]
+                if existing_features['descriptors'].shape[1] != descriptors.shape[1]:
+                    print(f"Warning: Descriptor dimension mismatch ({existing_features['descriptors'].shape[1]} vs {descriptors.shape[1]}). Cannot concatenate. Replacing features.")
+                    # Replace instead of concatenate when dimensions don't match
+                    existing_features['keypoints'] = keypoints
+                    existing_features['descriptors'] = descriptors
+                    existing_features['positions'] = mapped_positions
+                else:
+                    # Append to existing features
+                    existing_features['keypoints'] = np.concatenate([existing_features['keypoints'], keypoints])
+                    existing_features['descriptors'] = np.concatenate([existing_features['descriptors'], descriptors])
+                    existing_features['positions'].extend(mapped_positions)
+                print(f"Added {len(keypoints)} features to frame {frame_number} (total: {len(existing_features['positions'])})")
+            else:
+                # Create new features entry
+                self.features[frame_number] = {
+                    'keypoints': keypoints,
+                    'descriptors': descriptors,
+                    'positions': mapped_positions
+                }
+                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 track_features_optical_flow(self, prev_frame, curr_frame, prev_points):
+        """Track features using Lucas-Kanade optical flow"""
+        try:
+            # Convert to grayscale if needed
+            if len(prev_frame.shape) == 3:
+                prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
+            else:
+                prev_gray = prev_frame
+                
+            if len(curr_frame.shape) == 3:
+                curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
+            else:
+                curr_gray = curr_frame
+            
+            # Parameters for Lucas-Kanade optical flow
+            lk_params = dict(winSize=(15, 15),
+                           maxLevel=2,
+                           criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
+            
+            # Calculate optical flow
+            new_points, status, _ = cv2.calcOpticalFlowPyrLK(prev_gray, curr_gray, prev_points, None, **lk_params)
+            
+            # Filter out bad tracks
+            good_new = new_points[status == 1]
+            good_old = prev_points[status == 1]
+            
+            return good_new, good_old, status
+            
+        except Exception as e:
+            print(f"Error in optical flow tracking: {e}")
+            return None, None, None
+    
+    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")

croppa/utils.py

@@ -0,0 +1,34 @@
+import cv2
+import ctypes
+import numpy as np
+from PIL import Image
+
+
+def load_image_utf8(image_path):
+    """Load image with UTF-8 path support using PIL, then convert to OpenCV format"""
+    try:
+        # Use PIL to load image with UTF-8 support
+        pil_image = Image.open(image_path)
+        # Convert PIL image to OpenCV format (BGR)
+        cv_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+        return cv_image
+    except Exception as e:
+        raise ValueError(f"Could not load image file: {image_path} - {e}")
+
+
+def get_active_window_title():
+    """Get the title of the currently active window"""
+    try:
+        # Get handle to foreground window
+        hwnd = ctypes.windll.user32.GetForegroundWindow()
+        
+        # Get window title length
+        length = ctypes.windll.user32.GetWindowTextLengthW(hwnd)
+        
+        # Create buffer and get window title
+        buffer = ctypes.create_unicode_buffer(length + 1)
+        ctypes.windll.user32.GetWindowTextW(hwnd, buffer, length + 1)
+        
+        return buffer.value
+    except:
+        return ""

pyproject.toml

@@ -5,6 +5,8 @@ description = "Media Grader - Grade media files by moving them to numbered folde
 requires-python = ">=3.13"
 dependencies = [
     "opencv-python>=4.12.0.88",
+    "ruff>=0.12.12",
+    "vulture>=2.14",
 ]
 
 [project.scripts]
@@ -21,3 +23,7 @@ include = ["main.py"]
 members = [
     "croppa",
 ]
+
+[tool.ruff]
+# Ensure F841 is enabled (it's part of default linting)
+select = ["F841"]

uv.lock

@@ -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]]
@@ -29,10 +31,16 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "opencv-python" },
+    { name = "ruff" },
+    { name = "vulture" },
 ]
 
 [package.metadata]
-requires-dist = [{ name = "opencv-python", specifier = ">=4.12.0.88" }]
+requires-dist = [
+    { name = "opencv-python", specifier = ">=4.12.0.88" },
+    { name = "ruff", specifier = ">=0.12.12" },
+    { name = "vulture", specifier = ">=2.14" },
+]
 
 [[package]]
 name = "numpy"
@@ -78,3 +86,93 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/02/96/213fea371d3cb2f1d537612a105792aa0a6659fb2665b22cad709a75bd94/opencv_python-4.12.0.88-cp37-abi3-win32.whl", hash = "sha256:ff554d3f725b39878ac6a2e1fa232ec509c36130927afc18a1719ebf4fbf4357", size = 30284131, upload-time = "2025-07-07T09:14:08.819Z" },
     { url = "https://files.pythonhosted.org/packages/fa/80/eb88edc2e2b11cd2dd2e56f1c80b5784d11d6e6b7f04a1145df64df40065/opencv_python-4.12.0.88-cp37-abi3-win_amd64.whl", hash = "sha256:d98edb20aa932fd8ebd276a72627dad9dc097695b3d435a4257557bbb49a79d2", size = 39000307, upload-time = "2025-07-07T09:14:16.641Z" },
 ]
+
+[[package]]
+name = "pillow"
+version = "11.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/0d/d0d6dea55cd152ce3d6767bb38a8fc10e33796ba4ba210cbab9354b6d238/pillow-11.3.0.tar.gz", hash = "sha256:3828ee7586cd0b2091b6209e5ad53e20d0649bbe87164a459d0676e035e8f523", size = 47113069, upload-time = "2025-07-01T09:16:30.666Z" }
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