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import os
from pathlib import Path
from typing import List, Union, Tuple
from PIL import Image
import ezdxf.units
import numpy as np
import torch
from torchvision import transforms
from ultralytics import YOLOWorld, YOLO
from ultralytics.engine.results import Results
from ultralytics.utils.plotting import save_one_box
from transformers import AutoModelForImageSegmentation
import cv2
import ezdxf
import gradio as gr
import gc
# from scalingtestupdated import calculate_scaling_factor
from scalingtestupdated import calculate_scaling_factor_with_units, calculate_paper_scaling_factor, convert_units, calculate_paper_scaling_factor_corrected
from scipy.interpolate import splprep, splev
from scipy.ndimage import gaussian_filter1d
import json
import time
import signal
from shapely.ops import unary_union
from shapely.geometry import MultiPolygon, GeometryCollection, Polygon, Point
from u2netp import U2NETP
import logging
import shutil
import sys
# Add this at the very beginning of your main Python file, before any other imports
os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '0'
os.environ['OPENCV_IO_ENABLE_JASPER'] = '0'
os.environ['QT_QPA_PLATFORM'] = 'offscreen'
os.environ['MPLBACKEND'] = 'Agg'
# For headless environments
import matplotlib
matplotlib.use('Agg')
# Initialize logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Create cache directory for models
CACHE_DIR = os.path.join(os.path.dirname(__file__), ".cache")
os.makedirs(CACHE_DIR, exist_ok=True)
# Paper size configurations (in mm)
PAPER_SIZES = {
"A4": {"width": 210, "height": 297},
"A3": {"width": 297, "height": 420},
"US Letter": {"width": 215.9, "height": 279.4}
}
# Custom Exception Classes
class TimeoutReachedError(Exception):
pass
class BoundaryOverlapError(Exception):
pass
class TextOverlapError(Exception):
pass
class PaperNotDetectedError(Exception):
"""Raised when the paper cannot be detected in the image"""
pass
class MultipleObjectsError(Exception):
"""Raised when multiple objects are detected on the paper"""
def __init__(self, message="Multiple objects detected. Please place only a single object on the paper."):
super().__init__(message)
class NoObjectDetectedError(Exception):
"""Raised when no object is detected on the paper"""
def __init__(self, message="No object detected on the paper. Please ensure an object is placed on the paper."):
super().__init__(message)
class FingerCutOverlapError(Exception):
"""Raised when finger cuts overlap with existing geometry"""
def __init__(self, message="There was an overlap with fingercuts... Please try again to generate dxf."):
super().__init__(message)
class ReferenceBoxNotDetectedError(Exception):
"""Raised when reference box/paper cannot be detected"""
def __init__(self, message="Reference box not detected"):
super().__init__(message)
# Global model variables for lazy loading
paper_detector_global = None
u2net_global = None
birefnet = None
# Model paths
paper_model_path = os.path.join(CACHE_DIR, "paper_detector.pt") # You'll need to train/provide this
u2net_model_path = os.path.join(CACHE_DIR, "u2netp.pth")
# Global variable for YOLOWorld
yolo_v8_global = None
yolo_v8_model_path = os.path.join(CACHE_DIR, "yolov8n.pt") # Adjust path as needed
# Device configuration
device = "cpu"
torch.set_float32_matmul_precision(["high", "highest"][0])
def ensure_model_files():
"""Ensure model files are available in cache directory"""
if not os.path.exists(paper_model_path):
if os.path.exists("paper_detector.pt"):
shutil.copy("paper_detector.pt", paper_model_path)
else:
logger.warning("paper_detector.pt model file not found - using fallback detection")
if not os.path.exists(u2net_model_path):
if os.path.exists("u2netp.pth"):
shutil.copy("u2netp.pth", u2net_model_path)
else:
raise FileNotFoundError("u2netp.pth model file not found")
logger.info("YOLOv8 will auto-download if not present")
ensure_model_files()
# Lazy loading functions
def get_paper_detector():
"""Lazy load paper detector model"""
global paper_detector_global
if paper_detector_global is None:
logger.info("Loading paper detector model...")
if os.path.exists(paper_model_path):
try:
paper_detector_global = YOLO(paper_model_path)
logger.info("Paper detector loaded successfully")
except Exception as e:
logger.error(f"Failed to load paper detector: {e}")
paper_detector_global = None
else:
# Fallback to generic object detection for paper-like rectangles
logger.warning("Paper model file not found, using fallback detection")
paper_detector_global = None
return paper_detector_global
def get_yolo_v8():
"""Lazy load YOLOv8 model"""
global yolo_v8_global
if yolo_v8_global is None:
logger.info("Loading YOLOv8 model...")
try:
yolo_v8_global = YOLO(yolo_v8_model_path) # Auto-downloads if needed
logger.info("YOLOv8 model loaded successfully")
except Exception as e:
logger.error(f"Failed to load YOLOv8: {e}")
yolo_v8_global = None
return yolo_v8_global
def get_u2net():
"""Lazy load U2NETP model"""
global u2net_global
if u2net_global is None:
logger.info("Loading U2NETP model...")
u2net_global = U2NETP(3, 1)
u2net_global.load_state_dict(torch.load(u2net_model_path, map_location="cpu"))
u2net_global.to(device)
u2net_global.eval()
logger.info("U2NETP model loaded successfully")
return u2net_global
def load_birefnet_model():
"""Load BiRefNet model from HuggingFace"""
return AutoModelForImageSegmentation.from_pretrained(
'ZhengPeng7/BiRefNet',
trust_remote_code=True
)
def get_birefnet():
"""Lazy load BiRefNet model"""
global birefnet
if birefnet is None:
logger.info("Loading BiRefNet model...")
birefnet = load_birefnet_model()
birefnet.to(device)
birefnet.eval()
logger.info("BiRefNet model loaded successfully")
return birefnet
def detect_paper_contour(image: np.ndarray, output_unit: str = "mm") -> Tuple[np.ndarray, float]:
"""
Detect paper in the image using contour detection as fallback
Returns the paper contour and estimated scaling factor
"""
logger.info("Using contour-based paper detection")
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image
# Apply bilateral filter to reduce noise while preserving edges
filtered = cv2.bilateralFilter(gray, 9, 75, 75)
# Apply adaptive threshold
thresh = cv2.adaptiveThreshold(filtered, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
# Edge detection with multiple thresholds
edges1 = cv2.Canny(filtered, 50, 150)
edges2 = cv2.Canny(filtered, 30, 100)
edges = cv2.bitwise_or(edges1, edges2)
# Morphological operations to connect broken edges
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
# Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Filter contours by area and aspect ratio to find paper-like rectangles
paper_contours = []
image_area = image.shape[0] * image.shape[1]
min_area = image_area * 0.20 # At least 15% of image
max_area = image_area * 0.85 # At most 95% of image
for contour in contours:
area = cv2.contourArea(contour)
if min_area < area < max_area:
# Approximate contour to polygon
epsilon = 0.015 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
# Check if it's roughly rectangular (4 corners) or close to it
if len(approx) >= 4:
# Calculate bounding rectangle
rect = cv2.boundingRect(approx)
w, h = rect[2], rect[3]
aspect_ratio = w / h if h > 0 else 0
# Check if aspect ratio matches common paper ratios
# A4: 1.414, A3: 1.414, US Letter: 1.294
if 1.3 < aspect_ratio < 1.5: # More lenient tolerance
# Check if contour area is close to bounding rect area (rectangularity)
rect_area = w * h
if rect_area > 0:
extent = area / rect_area
if extent > 0.85: # At least 85% rectangular
paper_contours.append((contour, area, aspect_ratio, extent))
if not paper_contours:
logger.error("No paper-like contours found")
raise ReferenceBoxNotDetectedError("Could not detect paper in the image using contour detection")
# Select the best paper contour based on area and rectangularity
paper_contours.sort(key=lambda x: (x[1] * x[3]), reverse=True) # Sort by area * extent
best_contour = paper_contours[0][0]
logger.info(f"Paper detected using contours: area={paper_contours[0][1]}, aspect_ratio={paper_contours[0][2]:.2f}")
# Return 0.0 as placeholder - will be calculated later based on paper size
return best_contour, 0.0
def detect_paper_bounds(image: np.ndarray, paper_size: str, output_unit: str = "mm") -> Tuple[np.ndarray, float]:
"""
Detect paper bounds in the image and calculate scaling factor
"""
try:
paper_detector = get_paper_detector()
if paper_detector is not None:
# Use trained model if available
results = paper_detector.predict(image, conf=0.8, verbose=False)
if not results or len(results) == 0:
logger.warning("No results from paper detector")
return detect_paper_contour(image, output_unit)
# Check if boxes exist and are not empty
if not hasattr(results[0], 'boxes') or results[0].boxes is None or len(results[0].boxes) == 0:
logger.warning("No boxes detected by model, using fallback contour detection")
return detect_paper_contour(image, output_unit)
# Get the largest detected paper
boxes = results[0].boxes.xyxy.cpu().numpy()
if len(boxes) == 0:
logger.warning("Empty boxes detected, using fallback")
return detect_paper_contour(image, output_unit)
largest_box = None
max_area = 0
for box in boxes:
x_min, y_min, x_max, y_max = box
area = (x_max - x_min) * (y_max - y_min)
if area > max_area:
max_area = area
largest_box = box
if largest_box is None:
logger.warning("No valid paper box found, using fallback")
return detect_paper_contour(image, output_unit)
# Convert box to contour-like format
x_min, y_min, x_max, y_max = map(int, largest_box)
paper_contour = np.array([
[[x_min, y_min]],
[[x_max, y_min]],
[[x_max, y_max]],
[[x_min, y_max]]
])
logger.info(f"Paper detected by model: {x_min},{y_min} to {x_max},{y_max}")
else:
# Use fallback contour detection
logger.info("Using fallback contour detection for paper")
paper_contour, _ = detect_paper_contour(image, output_unit)
# After getting paper_contour, expand it
rect = cv2.boundingRect(paper_contour)
expansion = int(min(rect[2], rect[3]) * 0.1) # Expand by 10%
x, y, w, h = rect
expanded_contour = np.array([
[[max(0, x - expansion), max(0, y - expansion)]],
[[min(image.shape[1], x + w + expansion), max(0, y - expansion)]],
[[min(image.shape[1], x + w + expansion), min(image.shape[0], y + h + expansion)]],
[[max(0, x - expansion), min(image.shape[0], y + h + expansion)]]
])
paper_contour = expanded_contour
# Calculate scaling factor based on paper size with proper units
# scaling_factor = calculate_paper_scaling_factor(paper_contour, paper_size, output_unit)
scaling_factor, unit_string = calculate_paper_scaling_factor_corrected(
paper_contour,
paper_size,
output_unit="mm",
correction_factor=1.235, # Adjust this value
method="average" # Try different methods
)
return paper_contour, scaling_factor
except Exception as e:
logger.error(f"Error in paper detection: {e}")
raise ReferenceBoxNotDetectedError(f"Failed to detect paper: {str(e)}")
def calculate_paper_scaling_factor(paper_contour: np.ndarray, paper_size: str, output_unit: str = "mm") -> float:
"""
Calculate scaling factor based on detected paper dimensions with proper unit handling.
"""
from scalingtestupdated import calculate_paper_scaling_factor as calc_paper_scale
scaling_factor, unit_string = calc_paper_scale(paper_contour, paper_size, output_unit)
return scaling_factor
def validate_single_object(mask: np.ndarray, paper_contour: np.ndarray) -> None:
"""
Validate that only a single object is present on the paper
"""
# Create a mask for the paper area
paper_mask = np.zeros(mask.shape[:2], dtype=np.uint8)
cv2.fillPoly(paper_mask, [paper_contour], 255)
# Apply paper mask to object mask
masked_objects = cv2.bitwise_and(mask, paper_mask)
# Find contours of objects within paper bounds
contours, _ = cv2.findContours(masked_objects, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Filter out very small contours (noise) and paper-sized contours
image_area = mask.shape[0] * mask.shape[1]
min_area = 100 # Minimum area threshold
max_area = image_area * 0.5 # Maximum 50% of image area (to exclude paper detection)
significant_contours = [c for c in contours if min_area < cv2.contourArea(c) < max_area]
if len(significant_contours) == 0:
raise NoObjectDetectedError()
elif len(significant_contours) > 1:
raise MultipleObjectsError()
logger.info(f"Single object validated: {len(significant_contours)} significant contour(s) found")
def remove_bg_u2netp(image: np.ndarray) -> np.ndarray:
"""Remove background using U2NETP model"""
try:
u2net_model = get_u2net()
image_pil = Image.fromarray(image)
transform_u2netp = transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
input_tensor = transform_u2netp(image_pil).unsqueeze(0).to(device)
with torch.no_grad():
outputs = u2net_model(input_tensor)
pred = outputs[0]
pred = (pred - pred.min()) / (pred.max() - pred.min() + 1e-8)
pred_np = pred.squeeze().cpu().numpy()
pred_np = cv2.resize(pred_np, (image_pil.width, image_pil.height))
pred_np = (pred_np * 255).astype(np.uint8)
return pred_np
except Exception as e:
logger.error(f"Error in U2NETP background removal: {e}")
raise
def remove_bg(image: np.ndarray) -> np.ndarray:
"""Remove background using BiRefNet model for main objects"""
try:
birefnet_model = get_birefnet()
transform_image = transforms.Compose([
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
image_pil = Image.fromarray(image)
input_images = transform_image(image_pil).unsqueeze(0).to(device)
with torch.no_grad():
preds = birefnet_model(input_images)[-1].sigmoid().cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
scale_ratio = 1024 / max(image_pil.size)
scaled_size = (int(image_pil.size[0] * scale_ratio), int(image_pil.size[1] * scale_ratio))
return np.array(pred_pil.resize(scaled_size))
except Exception as e:
logger.error(f"Error in BiRefNet background removal: {e}")
raise
def mask_paper_area_in_image(image: np.ndarray, paper_contour: np.ndarray) -> np.ndarray:
"""Less aggressive masking to preserve corner objects"""
masked_image = image.copy()
# Much less aggressive shrinking - only 2% instead of 8%
rect = cv2.boundingRect(paper_contour)
shrink_pixels = max(5, int(min(rect[2], rect[3]) * 0.02)) # Changed from 0.08 to 0.02
x, y, w, h = rect
# Create mask but keep more area
outer_mask = np.ones(image.shape[:2], dtype=np.uint8) * 255
inner_contour = np.array([
[[x + shrink_pixels, y + shrink_pixels]],
[[x + w - shrink_pixels, y + shrink_pixels]],
[[x + w - shrink_pixels, y + h - shrink_pixels]],
[[x + shrink_pixels, y + h - shrink_pixels]]
])
cv2.fillPoly(outer_mask, [inner_contour], 0)
masked_image[outer_mask == 255] = [128, 128, 128] # Gray instead of black
return masked_image
def exclude_paper_area(mask: np.ndarray, paper_contour: np.ndarray, expansion_factor: float = 1.2) -> np.ndarray:
"""Less aggressive paper area exclusion"""
# Create paper mask
paper_mask = np.zeros(mask.shape[:2], dtype=np.uint8)
cv2.fillPoly(paper_mask, [paper_contour], 255)
# Instead of eroding, slightly expand the paper mask
rect = cv2.boundingRect(paper_contour)
expansion = max(10, int(min(rect[2], rect[3]) * 0.02)) # 2% expansion
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (expansion, expansion))
expanded_paper_mask = cv2.dilate(paper_mask, kernel, iterations=1)
# Keep objects within expanded paper area
result_mask = cv2.bitwise_and(mask, expanded_paper_mask)
return result_mask
def resample_contour(contour, edge_radius_px: int = 0):
"""Resample contour with radius-aware smoothing and periodic handling."""
logger.info(f"Starting resample_contour with contour of shape {contour.shape}")
num_points = 1500
sigma = max(2, int(edge_radius_px) // 4)
if len(contour) < 4:
error_msg = f"Contour must have at least 4 points, but has {len(contour)} points."
logger.error(error_msg)
raise ValueError(error_msg)
try:
contour = contour[:, 0, :]
logger.debug(f"Reshaped contour to shape {contour.shape}")
if not np.array_equal(contour[0], contour[-1]):
contour = np.vstack([contour, contour[0]])
tck, u = splprep(contour.T, u=None, s=0, per=True)
u_new = np.linspace(u.min(), u.max(), num_points)
x_new, y_new = splev(u_new, tck, der=0)
if sigma > 0:
x_new = gaussian_filter1d(x_new, sigma=sigma, mode='wrap')
y_new = gaussian_filter1d(y_new, sigma=sigma, mode='wrap')
x_new[-1] = x_new[0]
y_new[-1] = y_new[0]
result = np.array([x_new, y_new]).T
logger.info(f"Completed resample_contour with result shape {result.shape}")
return result
except Exception as e:
logger.error(f"Error in resample_contour: {e}")
raise
def save_dxf_spline(inflated_contours, scaling_factor, height, finger_clearance=False):
"""Save contours as DXF splines with optional finger cuts - scaling_factor should be in mm/px"""
doc = ezdxf.new(units=ezdxf.units.MM)
doc.header["$INSUNITS"] = ezdxf.units.MM
msp = doc.modelspace()
final_polygons_mm = [] # Use mm instead of inch for clarity
finger_centers = []
original_polygons = []
for contour in inflated_contours:
try:
resampled_contour = resample_contour(contour)
# Convert pixel coordinates to mm using the scaling factor
points_mm = [(x * scaling_factor, (height - y) * scaling_factor)
for x, y in resampled_contour]
if len(points_mm) < 3:
continue
tool_polygon = build_tool_polygon(points_mm)
original_polygons.append(tool_polygon)
if finger_clearance:
try:
tool_polygon, center = place_finger_cut_adjusted(
tool_polygon, points_mm, finger_centers, final_polygons_mm
)
except FingerCutOverlapError:
tool_polygon = original_polygons[-1]
exterior_coords = polygon_to_exterior_coords(tool_polygon)
if len(exterior_coords) < 3:
continue
# Coordinates are already in mm, so add directly to DXF
msp.add_spline(exterior_coords, degree=3, dxfattribs={"layer": "TOOLS"})
final_polygons_mm.append(tool_polygon)
except ValueError as e:
logger.warning(f"Skipping contour: {e}")
dxf_filepath = os.path.join("./outputs", "out.dxf")
doc.saveas(dxf_filepath)
return dxf_filepath, final_polygons_mm, original_polygons
def build_tool_polygon(points_inch):
"""Build a polygon from inch-converted points"""
return Polygon(points_inch)
def polygon_to_exterior_coords(poly):
"""Extract exterior coordinates from polygon"""
logger.info(f"Starting polygon_to_exterior_coords with input geometry type: {poly.geom_type}")
try:
if poly.geom_type == "GeometryCollection" or poly.geom_type == "MultiPolygon":
logger.debug(f"Performing unary_union on {poly.geom_type}")
unified = unary_union(poly)
if unified.is_empty:
logger.warning("unary_union produced an empty geometry; returning empty list")
return []
if unified.geom_type == "GeometryCollection" or unified.geom_type == "MultiPolygon":
largest = None
max_area = 0.0
for g in getattr(unified, "geoms", []):
if hasattr(g, "area") and g.area > max_area and hasattr(g, "exterior"):
max_area = g.area
largest = g
if largest is None:
logger.warning("No valid Polygon found in unified geometry; returning empty list")
return []
poly = largest
else:
poly = unified
if not hasattr(poly, "exterior") or poly.exterior is None:
logger.warning("Input geometry has no exterior ring; returning empty list")
return []
raw_coords = list(poly.exterior.coords)
total = len(raw_coords)
logger.info(f"Extracted {total} raw exterior coordinates")
if total == 0:
return []
# Subsample coordinates to at most 100 points
max_pts = 100
if total > max_pts:
step = total // max_pts
sampled = [raw_coords[i] for i in range(0, total, step)]
if sampled[-1] != raw_coords[-1]:
sampled.append(raw_coords[-1])
logger.info(f"Downsampled perimeter from {total} to {len(sampled)} points")
return sampled
else:
return raw_coords
except Exception as e:
logger.error(f"Error in polygon_to_exterior_coords: {e}")
return []
def place_finger_cut_adjusted(
tool_polygon: Polygon,
points_inch: list,
existing_centers: list,
all_polygons: list,
circle_diameter: float = 25.4,
min_gap: float = 0.5,
max_attempts: int = 100
) -> Tuple[Polygon, tuple]:
"""Place finger cuts with collision avoidance"""
logger.info(f"Starting place_finger_cut_adjusted with {len(points_inch)} input points")
def fallback_solution():
logger.warning("Using fallback approach for finger cut placement")
fallback_center = points_inch[len(points_inch) // 2]
r = circle_diameter / 2.0
fallback_circle = Point(fallback_center).buffer(r, resolution=32)
try:
union_poly = tool_polygon.union(fallback_circle)
except Exception as e:
logger.warning(f"Fallback union failed ({e}); trying buffer-union fallback")
union_poly = tool_polygon.buffer(0).union(fallback_circle.buffer(0))
existing_centers.append(fallback_center)
logger.info(f"Fallback finger cut placed at {fallback_center}")
return union_poly, fallback_center
r = circle_diameter / 2.0
needed_center_dist = circle_diameter + min_gap
raw_perimeter = polygon_to_exterior_coords(tool_polygon)
if not raw_perimeter:
logger.warning("No valid exterior coords found; using fallback immediately")
return fallback_solution()
if len(raw_perimeter) > 100:
step = len(raw_perimeter) // 100
perimeter_coords = raw_perimeter[::step]
logger.info(f"Subsampled perimeter from {len(raw_perimeter)} to {len(perimeter_coords)} points")
else:
perimeter_coords = raw_perimeter[:]
indices = list(range(len(perimeter_coords)))
np.random.shuffle(indices)
logger.debug(f"Shuffled perimeter indices for candidate order")
start_time = time.time()
timeout_secs = 5.0
attempts = 0
try:
while attempts < max_attempts:
if time.time() - start_time > timeout_secs - 0.1:
logger.warning(f"Approaching timeout after {attempts} attempts")
return fallback_solution()
for idx in indices:
if time.time() - start_time > timeout_secs - 0.05:
logger.warning("Timeout during candidate-point loop")
return fallback_solution()
cx, cy = perimeter_coords[idx]
for dx, dy in [(0, 0), (-min_gap/2, 0), (min_gap/2, 0), (0, -min_gap/2), (0, min_gap/2)]:
candidate_center = (cx + dx, cy + dy)
# Check distance to existing finger centers
too_close_finger = any(
np.hypot(candidate_center[0] - ex, candidate_center[1] - ey)
< needed_center_dist
for (ex, ey) in existing_centers
)
if too_close_finger:
continue
# Build candidate circle
candidate_circle = Point(candidate_center).buffer(r, resolution=32)
# Must overlap ≥30% with this polygon
try:
inter_area = tool_polygon.intersection(candidate_circle).area
except Exception:
continue
if inter_area < 0.3 * candidate_circle.area:
continue
# Must not intersect other polygons
invalid = False
for other_poly in all_polygons:
if other_poly.equals(tool_polygon):
continue
if other_poly.buffer(min_gap).intersects(candidate_circle) or \
other_poly.buffer(min_gap).touches(candidate_circle):
invalid = True
break
if invalid:
continue
# Union and return
try:
union_poly = tool_polygon.union(candidate_circle)
if union_poly.geom_type == "MultiPolygon" and len(union_poly.geoms) > 1:
continue
if union_poly.equals(tool_polygon):
continue
except Exception:
continue
existing_centers.append(candidate_center)
logger.info(f"Finger cut placed successfully at {candidate_center} after {attempts} attempts")
return union_poly, candidate_center
attempts += 1
if attempts >= (max_attempts // 2) and (time.time() - start_time) > timeout_secs * 0.8:
logger.warning(f"Approaching timeout (attempt {attempts})")
return fallback_solution()
logger.warning(f"No valid spot after {max_attempts} attempts, using fallback")
return fallback_solution()
except Exception as e:
logger.error(f"Error in place_finger_cut_adjusted: {e}")
return fallback_solution()
# def extract_outlines(binary_image: np.ndarray) -> Tuple[np.ndarray, list]:
# """Extract outlines from binary image"""
# contours, _ = cv2.findContours(
# binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
# )
# outline_image = np.full_like(binary_image, 255)
# return outline_image, contours
def extract_outlines(binary_image: np.ndarray) -> Tuple[np.ndarray, list]:
"""Extract outlines from binary image"""
# Check if contours are being cut at image boundaries
h, w = binary_image.shape
# Add small border to prevent boundary cutting
bordered_image = cv2.copyMakeBorder(binary_image, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=0)
contours, _ = cv2.findContours(
bordered_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
)
# Adjust contour coordinates back to original image space
adjusted_contours = []
for contour in contours:
adjusted_contour = contour - [5, 5] # Subtract border offset
adjusted_contours.append(adjusted_contour)
outline_image = np.full_like(binary_image, 255)
return outline_image, adjusted_contours
def round_edges(mask: np.ndarray, radius_mm: float, scaling_factor: float) -> np.ndarray:
"""Round mask edges using contour smoothing"""
if radius_mm <= 0 or scaling_factor <= 0:
return mask
radius_px = max(1, int(radius_mm / scaling_factor))
if np.count_nonzero(mask) < 500:
return cv2.dilate(cv2.erode(mask, np.ones((3,3))), np.ones((3,3)))
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = [c for c in contours if cv2.contourArea(c) > 100]
smoothed_contours = []
for contour in contours:
try:
resampled = resample_contour(contour, radius_px)
resampled = resampled.astype(np.int32).reshape((-1, 1, 2))
smoothed_contours.append(resampled)
except Exception as e:
logger.warning(f"Error smoothing contour: {e}")
smoothed_contours.append(contour)
rounded = np.zeros_like(mask)
cv2.drawContours(rounded, smoothed_contours, -1, 255, thickness=cv2.FILLED)
return rounded
def cleanup_memory():
"""Clean up memory after processing"""
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
logger.info("Memory cleanup completed")
def cleanup_models():
"""Unload models to free memory"""
global paper_detector_global, u2net_global, birefnet
if paper_detector_global is not None:
del paper_detector_global
paper_detector_global = None
if u2net_global is not None:
del u2net_global
u2net_global = None
if birefnet is not None:
del birefnet
birefnet = None
cleanup_memory()
def make_square(img: np.ndarray):
"""Make the image square by padding"""
height, width = img.shape[:2]
max_dim = max(height, width)
pad_height = (max_dim - height) // 2
pad_width = (max_dim - width) // 2
pad_height_extra = max_dim - height - 2 * pad_height
pad_width_extra = max_dim - width - 2 * pad_width
if len(img.shape) == 3:
padded = np.pad(
img,
(
(pad_height, pad_height + pad_height_extra),
(pad_width, pad_width + pad_width_extra),
(0, 0),
),
mode="edge",
)
else:
padded = np.pad(
img,
(
(pad_height, pad_height + pad_height_extra),
(pad_width, pad_width + pad_width_extra),
),
mode="edge",
)
return padded
def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=False):
"""Main prediction function using paper as reference"""
logger.info(f"Starting prediction with image shape: {image.shape}")
logger.info(f"Paper size: {paper_size}, Offset: {offset} {offset_unit}")
# Convert offset to mm for internal calculations (DXF generation expects mm)
if offset_unit == "inches":
offset_mm = convert_units(offset, "inches", "mm")
else:
offset_mm = offset
edge_radius = None
if edge_radius is None or edge_radius == 0:
edge_radius = 0.0001
if offset < 0:
raise gr.Error("Offset Value Can't be negative")
try:
# Detect paper bounds and calculate scaling factor (always in mm for DXF)
logger.info("Starting paper detection...")
paper_contour, scaling_factor = detect_paper_bounds(image, paper_size, output_unit="mm")
logger.info(f"Paper detected successfully with scaling factor: {scaling_factor:.6f} mm/px")
except ReferenceBoxNotDetectedError as e:
logger.error(f"Paper detection failed: {e}")
return (
None, None, None, None,
f"Error: {str(e)}"
)
except Exception as e:
logger.error(f"Unexpected error in paper detection: {e}")
raise gr.Error(f"Error processing image: {str(e)}")
try:
# Get paper bounds with expansion
rect = cv2.boundingRect(paper_contour)
expansion = max(20, int(min(rect[2], rect[3]) * 0.05)) # 5% expansion
x, y, w, h = rect
x_min = max(0, x - expansion)
y_min = max(0, y - expansion)
x_max = min(image.shape[1], x + w + expansion)
y_max = min(image.shape[0], y + h + expansion)
# Process the expanded paper area
cropped_image = image[y_min:y_max, x_min:x_max]
crop_offset = (x_min, y_min)
# Remove background
objects_mask = remove_bg(cropped_image)
# Resize mask back to cropped image size
target_height = y_max - y_min
target_width = x_max - x_min
objects_mask_resized = cv2.resize(objects_mask, (target_width, target_height))
# Place back in full image space
full_mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
full_mask[y_min:y_max, x_min:x_max] = objects_mask_resized
# Light filtering only - don't exclude paper area aggressively
# Just remove small noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
objects_mask = cv2.morphologyEx(full_mask, cv2.MORPH_OPEN, kernel)
# Debug: Save intermediate masks
cv2.imwrite("./debug/objects_mask_after_processing.jpg", objects_mask)
# Check if we actually have object pixels
object_pixels = np.count_nonzero(objects_mask)
if object_pixels < 300: # Minimum threshold
raise NoObjectDetectedError("No significant object detected")
# Validate single object
validate_single_object(objects_mask, paper_contour)
except (MultipleObjectsError, NoObjectDetectedError) as e:
return (
None, None, None, None,
f"Error: {str(e)}"
)
except Exception as e:
raise gr.Error(f"Error in object detection: {str(e)}")
# Apply edge rounding if specified
rounded_mask = objects_mask.copy()
# Apply dilation for offset - more precise calculation using mm values
if offset_mm > 0:
offset_pixels = max(1, int(round(float(offset_mm) / scaling_factor)))
if offset_pixels > 0:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (offset_pixels*2+1, offset_pixels*2+1))
dilated_mask = cv2.dilate(rounded_mask, kernel, iterations=1)
else:
dilated_mask = rounded_mask.copy()
else:
dilated_mask = rounded_mask.copy()
# Save original dilated mask for output
Image.fromarray(dilated_mask).save("./outputs/scaled_mask_original.jpg")
dilated_mask_orig = dilated_mask.copy()
# Extract contours
outlines, contours = extract_outlines(dilated_mask)
try:
# Generate DXF - scaling_factor should be in mm/px for proper DXF units
dxf, finger_polygons, original_polygons = save_dxf_spline(
contours,
scaling_factor, # This should be mm/px
image.shape[0], # Use original image height
finger_clearance=(finger_clearance == "On")
)
except FingerCutOverlapError as e:
raise gr.Error(str(e))
# Create annotated image
shrunked_img_contours = image.copy()
if finger_clearance == "On":
outlines = np.full_like(dilated_mask, 255)
for poly in finger_polygons:
try:
coords = np.array([
(int(x / scaling_factor), int(image.shape[0] - y / scaling_factor))
for x, y in poly.exterior.coords
], np.int32).reshape((-1, 1, 2))
cv2.drawContours(shrunked_img_contours, [coords], -1, (0, 255, 0), thickness=2)
cv2.drawContours(outlines, [coords], -1, 0, thickness=2)
except Exception as e:
logger.warning(f"Failed to draw finger cut: {e}")
continue
else:
outlines = np.full_like(dilated_mask, 255)
cv2.drawContours(shrunked_img_contours, contours, -1, (0, 255, 0), thickness=2)
cv2.drawContours(outlines, contours, -1, 0, thickness=2)
cleanup_models()
# Format scaling info with proper unit display
if offset_unit == "inches":
offset_display = f"{offset} inches ({offset_mm:.3f} mm)"
else:
offset_display = f"{offset} mm"
scale_info = f"Scale: {scaling_factor:.6f} mm/px | Paper: {paper_size} | Offset: {offset_display}"
return (
shrunked_img_contours,
outlines,
dxf,
dilated_mask_orig,
scale_info
)
def predict_full_paper(image, paper_size, offset_value_mm = 0.02,offset_unit='mm', enable_finger_cut='Off', selected_outputs=None):
finger_flag = "On" if enable_finger_cut == "On" else "Off"
# Always get all outputs from predict_with_paper
ann, outlines, dxf_path, mask, scale_info = predict_with_paper(
image,
paper_size,
offset=offset_value_mm,
offset_unit= offset_unit,
finger_clearance=finger_flag,
)
# Return based on selected outputs
return (
dxf_path, # Always return DXF
ann if "Annotated Image" in selected_outputs else None,
outlines if "Outlines" in selected_outputs else None,
mask if "Mask" in selected_outputs else None,
scale_info # Always return scaling info
)
# Gradio Interface
if __name__ == "__main__":
os.makedirs("./outputs", exist_ok=True)
with gr.Blocks(title="Paper-Based DXF Generator", theme=gr.themes.Soft()) as demo:
# Example gallery
gr.Markdown("""
# Paper-Based DXF Generator
Upload an image with a single object placed on paper (A4, A3, or US Letter).
The paper serves as a size reference for accurate DXF generation.
""")
with gr.Row():
gr.Markdown("""
**Instructions:**
1. Place a single object on paper
2. Select the correct paper size
3. Configure options as needed
4. Click Submit to generate DXF
""")
gr.Markdown("""
### Tips for Best Results:
- Ensure good lighting and clear paper edges
- Place object completely at the center of the paper
- Avoid shadows that might interfere with detection
- Use high contrast between object and paper
""")
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Input Image (Object on Paper)",
type="numpy",
height=400
)
paper_size = gr.Radio(
choices=["A4", "A3", "US Letter"],
value="A4",
label="Paper Size",
info="Select the paper size used in your image"
)
# with gr.Group():
# gr.Markdown("### Contour Offset")
# with gr.Row():
# offset_value_mm = gr.Number(
# value=0.02,
# label="Offset",
# info="Expand contours outward by this amount",
# precision=3,
# minimum=0,
# maximum=50
# )
# offset_unit = gr.Dropdown(
# choices=["mm", "inches"],
# value="mm",
# label="Unit"
# )
# with gr.Group():
# gr.Markdown("### Finger Cuts")
# enable_finger_cut = gr.Radio(
# choices=["On", "Off"],
# value="Off",
# label="Enable Finger Cuts",
# info="Add circular cuts for easier handling"
# )
output_options = gr.CheckboxGroup(
choices=["Annotated Image", "Outlines", "Mask"],
value=[],
label="Additional Outputs",
info="DXF is always included"
)
submit_btn = gr.Button("Generate DXF", variant="primary", size="lg")
with gr.Column():
with gr.Group():
gr.Markdown("### Generated Files")
dxf_file = gr.File(label="DXF File", file_types=[".dxf"])
scale_info = gr.Textbox(label="Scaling Information", interactive=False)
with gr.Group():
gr.Markdown("### Preview Images")
output_image = gr.Image(label="Annotated Image", visible=False)
outlines_image = gr.Image(label="Outlines", visible=False)
mask_image = gr.Image(label="Mask", visible=False)
def update_outputs_visibility(selected):
return [
gr.update(visible="Annotated Image" in selected),
gr.update(visible="Outlines" in selected),
gr.update(visible="Mask" in selected)
]
output_options.change(
fn=update_outputs_visibility,
inputs=output_options,
outputs=[output_image, outlines_image, mask_image]
)
submit_btn.click(
fn=predict_full_paper,
inputs=[
input_image,
paper_size,
gr.Number(value=0.02, visible=False), # Create hidden components
gr.Textbox(value='mm', visible=False),
gr.Textbox(value='Off', visible=False),
output_options
],
outputs=[dxf_file, output_image, outlines_image, mask_image, scale_info]
)
demo.launch(share=True) |