run gui script
This commit is contained in:
17
README.md
17
README.md
@ -48,6 +48,23 @@ cd saw_mill_knot_detection
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python -m venv .venv
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source .venv/bin/activate
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# Install dependencies
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pip install -r requirements.txt
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```
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### 2. Run the Annotation GUI
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The repository includes an automated script that handles virtual environment activation:
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```bash
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# Run the GUI (automatically detects and activates venv/conda environment)
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./run_gui.sh
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# Or run manually
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source .venv/bin/activate # or conda activate your_env
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python annotation_gui.py
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```
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# Install dependencies
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pip install -U pip
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pip install ultralytics gradio rfdetr
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@ -328,14 +328,14 @@ class AnnotationApp:
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return f"❌ Model '{model_display}' not found"
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def load_new_images_dir(self, images_dir: str) -> tuple[Image.Image | None, str, str]:
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def load_new_images_dir(self, images_dir: str) -> tuple[str, str, str]:
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"""Load a new images directory from the GUI."""
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path = Path(images_dir)
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if not path.exists():
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return None, "", f"❌ Directory not found: {images_dir}"
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return "<div>Directory not found</div>", "", f"❌ Directory not found: {images_dir}"
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if not path.is_dir():
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return None, "", f"❌ Not a directory: {images_dir}"
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return "<div>Not a directory</div>", "", f"❌ Not a directory: {images_dir}"
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result = self._load_images(path)
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@ -343,12 +343,12 @@ class AnnotationApp:
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if self.image_paths:
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img, filename = self.get_current_image()
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boxes = self.annotations.get(filename, [])
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img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img
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img_html = self.generate_interactive_canvas(boxes)
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boxes_text = self._format_boxes_text(boxes)
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info = f"{result}\nImage 1/{len(self.image_paths)}: {filename}"
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return img_with_boxes, boxes_text, info
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return img_html, boxes_text, info
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else:
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return None, "", f"{result}\n⚠️ No .jpg or .png images found in directory"
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return "<div>No images found</div>", "", f"{result}\n⚠️ No .jpg or .png images found in directory"
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def get_current_model_info(self) -> str:
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"""Get info about currently loaded model."""
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@ -385,73 +385,285 @@ class AnnotationApp:
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# Draw box
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draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
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# Draw corner handles for editing (small squares)
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handle_size = 6
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draw.rectangle([x1-handle_size, y1-handle_size, x1+handle_size, y1+handle_size], fill="red")
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draw.rectangle([x2-handle_size, y1-handle_size, x2+handle_size, y1+handle_size], fill="red")
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draw.rectangle([x1-handle_size, y2-handle_size, x1+handle_size, y2+handle_size], fill="red")
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draw.rectangle([x2-handle_size, y2-handle_size, x2+handle_size, y2+handle_size], fill="red")
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# Draw label
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text = f"{label} {conf:.2f}" if conf < 1.0 else label
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draw.text((x1, y1 - 20), text, fill="red")
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return img_draw
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def auto_label_current(self, threshold: float = 0.5) -> tuple[Image.Image, str, str]:
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"""Auto-label current image using loaded model."""
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if not self.model:
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return None, "", "❌ No model loaded"
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def generate_interactive_canvas(self, boxes: list[dict] = None) -> str:
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"""Generate HTML with interactive canvas for annotation."""
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img, filename = self.get_current_image()
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if not img:
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return None, "", "No images"
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return "<div>No image loaded</div>"
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try:
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# Run inference based on model type
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if self.current_model_type == "rf-detr":
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# RF-DETR custom prediction
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detections = self.model.predict(img, threshold=threshold)
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boxes = []
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for i in range(len(detections)):
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xyxy = detections.xyxy[i]
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conf = float(detections.confidence[i]) if detections.confidence is not None else 1.0
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x1, y1, x2, y2 = xyxy
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boxes.append({
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"bbox": [float(x1), float(y1), float(x2), float(y2)],
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"label": "knot",
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"confidence": conf,
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"source": "auto"
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})
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if boxes is None:
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boxes = self.annotations.get(filename, [])
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# Resize image for display if too large
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max_width = 1200
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max_height = 800
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if img.width > max_width or img.height > max_height:
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ratio = min(max_width / img.width, max_height / img.height)
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display_width = int(img.width * ratio)
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display_height = int(img.height * ratio)
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img = img.resize((display_width, display_height), Image.Resampling.LANCZOS)
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else:
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# Ultralytics models (RT-DETR, YOLOv6, YOLOX)
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results = self.model.predict(
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source=img,
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conf=threshold,
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save=False,
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verbose=False
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)
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display_width = img.width
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display_height = img.height
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boxes = []
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for result in results:
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for box in result.boxes:
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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conf = float(box.conf[0])
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boxes.append({
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"bbox": [x1, y1, x2, y2],
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"label": "knot",
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"confidence": conf,
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"source": "auto"
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})
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# Convert PIL image to base64
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import base64
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from io import BytesIO
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buffer = BytesIO()
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img.save(buffer, format="PNG", optimize=True)
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img_base64 = base64.b64encode(buffer.getvalue()).decode()
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# Add to existing annotations
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if filename not in self.annotations:
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self.annotations[filename] = []
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self.annotations[filename].extend(boxes)
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self._save_annotations()
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# Generate HTML with canvas and JavaScript
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boxes_json = json.dumps(boxes)
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# Redraw
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img_with_boxes = self.draw_boxes_on_image(img, self.annotations[filename])
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boxes_text = self._format_boxes_text(self.annotations[filename])
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info = f"🤖 Auto-labeled: {len(boxes)} detections | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
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html = f"""
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<div style="position: relative; display: inline-block; border: 1px solid #ccc; padding: 5px;">
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<div>Canvas Size: {display_width}x{display_height}</div>
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<canvas id="annotation-canvas" width="{display_width}" height="{display_height}"
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style="border: 1px solid #ccc; cursor: crosshair; max-width: 100%; height: auto; background-color: #f0f0f0;"></canvas>
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<div id="canvas-info" style="margin-top: 5px; font-size: 12px; color: #666;">
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Canvas ready - testing image loading
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</div>
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</div>
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<script>
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(function() {{
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const canvas = document.getElementById('annotation-canvas');
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const ctx = canvas.getContext('2d');
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const img = new Image();
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img.src = 'data:image/png;base64,{img_base64}';
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return img_with_boxes, boxes_text, info
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let boxes = {boxes_json};
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let isDragging = false;
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let dragStart = null;
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let selectedCorner = null;
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let selectedBoxIndex = -1;
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let creatingBox = false;
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let createStart = null;
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except Exception as e:
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return img, self._format_boxes_text(self.annotations.get(filename, [])), f"❌ Auto-label failed: {e}"
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// Handle size for high DPI displays
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const devicePixelRatio = window.devicePixelRatio || 1;
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const displayWidth = {display_width};
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const displayHeight = {display_height};
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// Temporarily disable high DPI scaling to fix image display
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// canvas.width = displayWidth * devicePixelRatio;
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// canvas.height = displayHeight * devicePixelRatio;
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// canvas.style.width = displayWidth + 'px';
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// canvas.style.height = displayHeight + 'px';
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// ctx.scale(devicePixelRatio, devicePixelRatio);
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img.onload = function() {{
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console.log('Image loaded successfully, size:', img.width, 'x', img.height);
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redraw();
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}};
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img.onerror = function() {{
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console.error('Failed to load image, drawing test pattern');
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// Draw a test pattern if image fails to load
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ctx.fillStyle = '#ffcccc';
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ctx.fillRect(0, 0, displayWidth, displayHeight);
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ctx.fillStyle = 'black';
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ctx.font = '20px Arial';
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ctx.fillText('Image failed to load', 10, 30);
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ctx.fillText('Canvas size: ' + displayWidth + 'x' + displayHeight, 10, 60);
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}};
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// Force redraw after a short delay
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setTimeout(function() {{
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console.log('Timeout check - image complete:', img.complete, 'natural size:', img.naturalWidth, 'x', img.naturalHeight);
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if (!img.complete || img.naturalWidth === 0) {{
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console.log('Image not loaded, drawing test pattern');
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ctx.fillStyle = '#ccccff';
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ctx.fillRect(0, 0, displayWidth, displayHeight);
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ctx.fillStyle = 'black';
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ctx.font = '16px Arial';
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ctx.fillText('Waiting for image...', 10, 30);
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ctx.fillText('Canvas: ' + displayWidth + 'x' + displayHeight, 10, 50);
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}}
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}}, 500);
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function redraw() {{
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ctx.clearRect(0, 0, displayWidth, displayHeight);
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ctx.drawImage(img, 0, 0, displayWidth, displayHeight);
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// Draw boxes
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boxes.forEach((box, index) => {{
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const [x1, y1, x2, y2] = box.bbox;
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const label = box.label || 'knot';
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const conf = box.confidence || 1.0;
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// Draw box
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ctx.strokeStyle = 'red';
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ctx.lineWidth = 3;
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ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
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// Draw corner handles
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const handleSize = 6;
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ctx.fillStyle = 'red';
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ctx.fillRect(x1 - handleSize, y1 - handleSize, handleSize * 2, handleSize * 2);
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ctx.fillRect(x2 - handleSize, y1 - handleSize, handleSize * 2, handleSize * 2);
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ctx.fillRect(x1 - handleSize, y2 - handleSize, handleSize * 2, handleSize * 2);
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ctx.fillRect(x2 - handleSize, y2 - handleSize, handleSize * 2, handleSize * 2);
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// Draw label
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ctx.fillStyle = 'red';
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ctx.font = '16px Arial';
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const text = conf < 1.0 ? `${{label}} ${{conf.toFixed(2)}}` : label;
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ctx.fillText(text, x1, y1 - 5);
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}});
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// Draw creation box if creating
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if (creatingBox && createStart && dragStart) {{
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ctx.strokeStyle = 'blue';
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ctx.lineWidth = 2;
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ctx.setLineDash([5, 5]);
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const x = Math.min(createStart.x, dragStart.x);
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const y = Math.min(createStart.y, dragStart.y);
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const w = Math.abs(createStart.x - dragStart.x);
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const h = Math.abs(createStart.y - dragStart.y);
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ctx.strokeRect(x, y, w, h);
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ctx.setLineDash([]);
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}}
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}}
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function getCornerAt(x, y) {{
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const handleSize = 6;
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for (let i = 0; i < boxes.length; i++) {{
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const [x1, y1, x2, y2] = boxes[i].bbox;
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const corners = [
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{{x: x1, y: y1, type: 'top-left'}},
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{{x: x2, y: y1, type: 'top-right'}},
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{{x: x1, y: y2, type: 'bottom-left'}},
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{{x: x2, y: y2, type: 'bottom-right'}}
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];
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for (let corner of corners) {{
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if (x >= corner.x - handleSize && x <= corner.x + handleSize &&
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y >= corner.y - handleSize && y <= corner.y + handleSize) {{
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return {{boxIndex: i, corner: corner.type, pos: corner}};
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}}
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}}
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}}
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return null;
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}}
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canvas.addEventListener('mousedown', function(e) {{
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const rect = canvas.getBoundingClientRect();
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const x = (e.clientX - rect.left) * (displayWidth / rect.width);
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const y = (e.clientY - rect.top) * (displayHeight / rect.height);
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selectedCorner = getCornerAt(x, y);
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if (selectedCorner) {{
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isDragging = true;
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selectedBoxIndex = selectedCorner.boxIndex;
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canvas.style.cursor = 'move';
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}} else {{
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// Start creating new box
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creatingBox = true;
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createStart = {{x: x, y: y}};
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dragStart = {{x: x, y: y}};
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canvas.style.cursor = 'crosshair';
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}}
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}});
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canvas.addEventListener('mousemove', function(e) {{
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const rect = canvas.getBoundingClientRect();
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const x = (e.clientX - rect.left) * (displayWidth / rect.width);
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const y = (e.clientY - rect.top) * (displayHeight / rect.height);
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if (isDragging && selectedCorner) {{
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const box = boxes[selectedBoxIndex];
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if (selectedCorner.corner === 'top-left') {{
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box.bbox[0] = Math.min(x, box.bbox[2] - 10); // x1
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box.bbox[1] = Math.min(y, box.bbox[3] - 10); // y1
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}} else if (selectedCorner.corner === 'top-right') {{
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box.bbox[2] = Math.max(x, box.bbox[0] + 10); // x2
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box.bbox[1] = Math.min(y, box.bbox[3] - 10); // y1
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}} else if (selectedCorner.corner === 'bottom-left') {{
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box.bbox[0] = Math.min(x, box.bbox[2] - 10); // x1
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box.bbox[3] = Math.max(y, box.bbox[1] + 10); // y2
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}} else if (selectedCorner.corner === 'bottom-right') {{
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box.bbox[2] = Math.max(x, box.bbox[0] + 10); // x2
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box.bbox[3] = Math.max(y, box.bbox[1] + 10); // y2
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}}
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document.getElementById('canvas-boxes-data').value = JSON.stringify(boxes);
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redraw();
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}} else if (creatingBox && createStart) {{
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dragStart = {{x: x, y: y}};
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redraw();
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}} else {{
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// Update cursor
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const corner = getCornerAt(x, y);
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if (corner) {{
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canvas.style.cursor = 'move';
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}} else {{
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canvas.style.cursor = 'crosshair';
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}}
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}}
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}});
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canvas.addEventListener('mouseup', function(e) {{
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if (creatingBox && createStart && dragStart) {{
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const x1 = Math.min(createStart.x, dragStart.x);
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const y1 = Math.min(createStart.y, dragStart.y);
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const x2 = Math.max(createStart.x, dragStart.x);
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const y2 = Math.max(createStart.y, dragStart.y);
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// Only add if box has minimum size
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if (x2 - x1 > 10 && y2 - y1 > 10) {{
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boxes.push({{
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bbox: [x1, y1, x2, y2],
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label: 'knot',
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confidence: 1.0,
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source: 'manual'
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}});
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document.getElementById('canvas-boxes-data').value = JSON.stringify(boxes);
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redraw();
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// Trigger save (this will be handled by the parent)
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if (window.saveAnnotations) {{
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window.saveAnnotations(boxes);
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}}
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}}
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}}
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isDragging = false;
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creatingBox = false;
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selectedCorner = null;
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selectedBoxIndex = -1;
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createStart = null;
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dragStart = null;
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canvas.style.cursor = 'crosshair';
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}});
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// Make boxes available globally for saving
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window.getCurrentBoxes = function() {{
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return JSON.stringify(boxes);
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}};
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// Function to update boxes from external call
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window.updateBoxes = function(newBoxes) {{
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boxes = newBoxes;
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redraw();
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}};
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}})();
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</script>
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"""
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return html
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def _format_boxes_text(self, boxes: list[dict]) -> str:
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"""Format boxes for display."""
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@ -467,7 +679,7 @@ class AnnotationApp:
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return "\n".join(lines)
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def load_image(self, direction: str = "current") -> tuple[Image.Image, str, str]:
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def load_image(self, direction: str = "current") -> tuple[str, str, str]:
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"""Load image (current/next/prev)."""
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if direction == "next":
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self.current_idx = min(self.current_idx + 1, len(self.image_paths) - 1)
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@ -476,21 +688,21 @@ class AnnotationApp:
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img, filename = self.get_current_image()
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if not img:
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return None, "", "No images"
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return "<div>No images</div>", "", "No images"
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# Load existing annotations
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boxes = self.annotations.get(filename, [])
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img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img
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img_html = self.generate_interactive_canvas(boxes)
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boxes_text = self._format_boxes_text(boxes)
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info = f"Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
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return img_with_boxes, boxes_text, info
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return img_html, boxes_text, info
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def add_box_manual(self, x1: int, y1: int, x2: int, y2: int) -> tuple[Image.Image, str, str]:
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def add_box_manual(self, x1: int, y1: int, x2: int, y2: int) -> tuple[str, str, str]:
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"""Manually add a bounding box."""
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img, filename = self.get_current_image()
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if not img:
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return None, "", "No images"
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return "<div>No images</div>", "", "No images"
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# Add box
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box = {
|
||||
@ -507,17 +719,17 @@ class AnnotationApp:
|
||||
|
||||
# Redraw
|
||||
boxes = self.annotations[filename]
|
||||
img_with_boxes = self.draw_boxes_on_image(img, boxes)
|
||||
img_html = self.generate_interactive_canvas(boxes)
|
||||
boxes_text = self._format_boxes_text(boxes)
|
||||
info = f"✓ Added box: {len(boxes)} total | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
|
||||
return img_with_boxes, boxes_text, info
|
||||
return img_html, boxes_text, info
|
||||
|
||||
def delete_last_box(self) -> tuple[Image.Image, str, str]:
|
||||
def delete_last_box(self) -> tuple[str, str, str]:
|
||||
"""Delete the last box from current image."""
|
||||
img, filename = self.get_current_image()
|
||||
if not img:
|
||||
return None, "", "No images"
|
||||
return "<div>No images</div>", "", "No images"
|
||||
|
||||
if filename in self.annotations and self.annotations[filename]:
|
||||
self.annotations[filename].pop()
|
||||
@ -525,34 +737,36 @@ class AnnotationApp:
|
||||
|
||||
# Redraw
|
||||
boxes = self.annotations.get(filename, [])
|
||||
img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img
|
||||
img_html = self.generate_interactive_canvas(boxes)
|
||||
boxes_text = self._format_boxes_text(boxes)
|
||||
info = f"✓ Deleted last box: {len(boxes)} remaining | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
|
||||
return img_with_boxes, boxes_text, info
|
||||
return img_html, boxes_text, info
|
||||
|
||||
def clear_boxes(self) -> tuple[Image.Image, str, str]:
|
||||
def clear_boxes(self) -> tuple[str, str, str]:
|
||||
"""Clear all boxes from current image."""
|
||||
img, filename = self.get_current_image()
|
||||
if not img:
|
||||
return None, "", "No images"
|
||||
return "<div>No images</div>", "", "No images"
|
||||
|
||||
self.annotations[filename] = []
|
||||
self._save_annotations()
|
||||
|
||||
boxes = []
|
||||
img_html = self.generate_interactive_canvas(boxes)
|
||||
boxes_text = "No annotations"
|
||||
info = f"✓ Cleared all boxes | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
|
||||
return img, boxes_text, info
|
||||
return img_html, boxes_text, info
|
||||
|
||||
def auto_label_current(self, threshold: float = 0.5) -> tuple[Image.Image, str, str]:
|
||||
def auto_label_current(self, threshold: float = 0.5) -> tuple[str, str, str]:
|
||||
"""Auto-label current image using loaded model."""
|
||||
if not self.model:
|
||||
return None, "", "❌ No model loaded"
|
||||
return "<div>No model loaded</div>", "", "❌ No model loaded"
|
||||
|
||||
img, filename = self.get_current_image()
|
||||
if not img:
|
||||
return None, "", "No images"
|
||||
return "<div>No images</div>", "", "No images"
|
||||
|
||||
try:
|
||||
# Run inference based on model type
|
||||
@ -598,14 +812,41 @@ class AnnotationApp:
|
||||
self._save_annotations()
|
||||
|
||||
# Redraw
|
||||
img_with_boxes = self.draw_boxes_on_image(img, self.annotations[filename])
|
||||
img_html = self.generate_interactive_canvas(self.annotations[filename])
|
||||
boxes_text = self._format_boxes_text(self.annotations[filename])
|
||||
info = f"🤖 Auto-labeled: {len(boxes)} detections | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
|
||||
return img_with_boxes, boxes_text, info
|
||||
return img_html, boxes_text, info
|
||||
|
||||
except Exception as e:
|
||||
return img, self._format_boxes_text(self.annotations.get(filename, [])), f"❌ Auto-label failed: {e}"
|
||||
boxes = self.annotations.get(filename, [])
|
||||
img_html = self.generate_interactive_canvas(boxes)
|
||||
return img_html, self._format_boxes_text(boxes), f"❌ Auto-label failed: {e}"
|
||||
|
||||
def save_canvas_changes(self, boxes_json: str) -> tuple[str, str, str]:
|
||||
"""Save changes made in the interactive canvas."""
|
||||
img, filename = self.get_current_image()
|
||||
if not img:
|
||||
return "<div>No images</div>", "", "No images"
|
||||
|
||||
try:
|
||||
# Parse the boxes from JSON
|
||||
if boxes_json:
|
||||
boxes = json.loads(boxes_json)
|
||||
self.annotations[filename] = boxes
|
||||
self._save_annotations()
|
||||
info = f"✓ Saved {len(boxes)} boxes | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
else:
|
||||
boxes = self.annotations.get(filename, [])
|
||||
info = f"✓ No changes to save | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
except json.JSONDecodeError:
|
||||
boxes = self.annotations.get(filename, [])
|
||||
info = f"❌ Invalid boxes data | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||||
|
||||
img_html = self.generate_interactive_canvas(boxes)
|
||||
boxes_text = self._format_boxes_text(boxes)
|
||||
|
||||
return img_html, boxes_text, info
|
||||
|
||||
def _save_annotations(self):
|
||||
"""Save annotations to JSON file."""
|
||||
@ -1041,12 +1282,16 @@ def create_ui(app: AnnotationApp) -> gr.Blocks:
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
image_display = gr.Image(label="Current Image", type="pil")
|
||||
image_display = gr.HTML(label="Current Image", value="<div style='width: 800px; height: 400px; border: 1px solid #ccc; display: flex; align-items: center; justify-content: center; color: #666;'>Load images from Settings to start annotating</div>")
|
||||
|
||||
with gr.Row():
|
||||
prev_btn = gr.Button("⬅️ Previous")
|
||||
next_btn = gr.Button("Next ➡️")
|
||||
auto_label_btn = gr.Button("🤖 Auto-Label", variant="primary")
|
||||
save_canvas_btn = gr.Button("💾 Save Canvas Changes")
|
||||
|
||||
# Hidden textbox to store canvas boxes data
|
||||
canvas_boxes_data = gr.Textbox(visible=False)
|
||||
|
||||
with gr.Row():
|
||||
threshold_slider = gr.Slider(0.1, 0.9, DEFAULT_DETECTION_THRESHOLD, label="Detection Threshold")
|
||||
@ -1276,6 +1521,19 @@ def create_ui(app: AnnotationApp) -> gr.Blocks:
|
||||
outputs=[image_display, boxes_text, info_text]
|
||||
)
|
||||
|
||||
save_canvas_btn.click(
|
||||
app.save_canvas_changes,
|
||||
inputs=[canvas_boxes_data],
|
||||
outputs=[image_display, boxes_text, info_text],
|
||||
js="""() => {
|
||||
const hiddenInput = document.getElementById('canvas-boxes-data');
|
||||
if (hiddenInput) {
|
||||
return hiddenInput.value;
|
||||
}
|
||||
return '';
|
||||
}"""
|
||||
)
|
||||
|
||||
add_box_btn.click(
|
||||
app.add_box_manual,
|
||||
inputs=[x1_input, y1_input, x2_input, y2_input],
|
||||
|
||||
Reference in New Issue
Block a user