from __future__ import annotations import os import gc import base64 import io import time import shutil import numpy as np import torch import cv2 import ezdxf from ezdxf.addons.text2path import make_paths_from_str from ezdxf import path from ezdxf.addons import text2path from ezdxf.enums import TextEntityAlignment from ezdxf.fonts.fonts import FontFace, get_font_face import gradio as gr from PIL import Image, ImageEnhance from pathlib import Path from typing import List, Union from ultralytics import YOLOWorld, YOLO from ultralytics.engine.results import Results from ultralytics.utils.plotting import save_one_box from transformers import AutoModelForImageSegmentation from torchvision import transforms from scalingtestupdated import calculate_scaling_factor from shapely.geometry import Polygon, Point, MultiPolygon from scipy.interpolate import splprep, splev from scipy.ndimage import gaussian_filter1d from u2net import U2NETP # --------------------- # Create a cache folder for models # --------------------- CACHE_DIR = os.path.join(os.path.dirname(__file__), ".cache") os.makedirs(CACHE_DIR, exist_ok=True) # --------------------- # Custom Exceptions # --------------------- class DrawerNotDetectedError(Exception): """Raised when the drawer cannot be detected in the image""" pass class ReferenceBoxNotDetectedError(Exception): """Raised when the Reference coin cannot be detected in the image""" pass class BoundaryOverlapError(Exception): """The specified boundary dimensions are too small and overlap with the inner contours.Please provide larger value for boundary length and width.""" pass class TextOverlapError(Exception): """Raised when the text overlaps with the inner contours (with a margin of 0.75).Please provide larger value for boundary length and width.""" pass class FingerCutOverlapError(Exception): """There was an overlap with fingercuts... Please try again to generate dxf.""" pass # --------------------- # Global Model Initialization with caching and print statements # --------------------- print("Loading YOLOWorld model...") start_time = time.time() yolo_model_path = os.path.join(CACHE_DIR, "yolov8x-worldv2.pt") if not os.path.exists(yolo_model_path): print("Caching YOLOWorld model to", yolo_model_path) shutil.copy("yolov8x-worldv2.pt", yolo_model_path) drawer_detector_global = YOLOWorld(yolo_model_path) drawer_detector_global.set_classes(["box"]) print("YOLOWorld model loaded in {:.2f} seconds".format(time.time() - start_time)) print("Loading YOLO reference model...") start_time = time.time() reference_model_path = os.path.join(CACHE_DIR, "coin_det.pt") if not os.path.exists(reference_model_path): print("Caching YOLO reference model to", reference_model_path) shutil.copy("coin_det.pt", reference_model_path) reference_detector_global = YOLO(reference_model_path) print("YOLO reference model loaded in {:.2f} seconds".format(time.time() - start_time)) print("Loading U²-Net model for reference background removal (U2NETP)...") start_time = time.time() u2net_model_path = os.path.join(CACHE_DIR, "u2netp.pth") if not os.path.exists(u2net_model_path): print("Caching U²-Net model to", u2net_model_path) shutil.copy("u2netp.pth", u2net_model_path) u2net_global = U2NETP(3, 1) u2net_global.load_state_dict(torch.load(u2net_model_path, map_location="cpu")) device = "cpu" u2net_global.to(device) u2net_global.eval() print("U²-Net model loaded in {:.2f} seconds".format(time.time() - start_time)) print("Loading BiRefNet model...") start_time = time.time() birefnet_global = AutoModelForImageSegmentation.from_pretrained( "zhengpeng7/BiRefNet", trust_remote_code=True, cache_dir=CACHE_DIR ) torch.set_float32_matmul_precision("high") birefnet_global.to(device) birefnet_global.eval() print("BiRefNet model loaded in {:.2f} seconds".format(time.time() - start_time)) # Define transform for BiRefNet transform_image_global = transforms.Compose([ transforms.Resize((1024, 1024)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]) # --------------------- # Model Reload Function (if needed) # --------------------- def unload_and_reload_models(): global drawer_detector_global, reference_detector_global, birefnet_global, u2net_global print("Reloading models...") start_time = time.time() del drawer_detector_global, reference_detector_global, birefnet_global, u2net_global gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() new_drawer_detector = YOLOWorld(os.path.join(CACHE_DIR, "yolov8x-worldv2.pt")) new_drawer_detector.set_classes(["box"]) new_reference_detector = YOLO(os.path.join(CACHE_DIR, "coin_det.pt")) new_birefnet = AutoModelForImageSegmentation.from_pretrained( "zhengpeng7/BiRefNet", trust_remote_code=True, cache_dir=CACHE_DIR ) new_birefnet.to(device) new_birefnet.eval() new_u2net = U2NETP(3, 1) new_u2net.load_state_dict(torch.load(os.path.join(CACHE_DIR, "u2netp.pth"), map_location="cpu")) new_u2net.to(device) new_u2net.eval() drawer_detector_global = new_drawer_detector reference_detector_global = new_reference_detector birefnet_global = new_birefnet u2net_global = new_u2net print("Models reloaded in {:.2f} seconds".format(time.time() - start_time)) # --------------------- # Helper Function: resize_img (defined once) # --------------------- def resize_img(img: np.ndarray, resize_dim): return np.array(Image.fromarray(img).resize(resize_dim)) # --------------------- # Other Helper Functions for Detection & Processing # --------------------- def yolo_detect(image: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor]) -> np.ndarray: t = time.time() results: List[Results] = drawer_detector_global.predict(image) if not results or len(results) == 0 or len(results[0].boxes) == 0: raise DrawerNotDetectedError("Drawer not detected in the image.") print("Drawer detection completed in {:.2f} seconds".format(time.time() - t)) return save_one_box(results[0].cpu().boxes.xyxy, im=results[0].orig_img, save=False) def detect_reference_square(img: np.ndarray): t = time.time() res = reference_detector_global.predict(img, conf=0.15) if not res or len(res) == 0 or len(res[0].boxes) == 0: raise ReferenceBoxNotDetectedError("Reference Coin not detected in the image.") print("Reference coin detection completed in {:.2f} seconds".format(time.time() - t)) return ( save_one_box(res[0].cpu().boxes.xyxy, res[0].orig_img, save=False), res[0].cpu().boxes.xyxy[0] ) # Use U2NETP for reference background removal. def remove_bg_u2netp(image: np.ndarray) -> np.ndarray: t = time.time() 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("cpu") with torch.no_grad(): outputs = u2net_global(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) print("U2NETP background removal completed in {:.2f} seconds".format(time.time() - t)) return pred_np # Use BiRefNet for main object background removal. def remove_bg(image: np.ndarray) -> np.ndarray: t = time.time() image_pil = Image.fromarray(image) input_images = transform_image_global(image_pil).unsqueeze(0).to("cpu") with torch.no_grad(): preds = birefnet_global(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)) result = np.array(pred_pil.resize(scaled_size)) print("BiRefNet background removal completed in {:.2f} seconds".format(time.time() - t)) return result def make_square(img: np.ndarray): 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 shrink_bbox(image: np.ndarray, shrink_factor: float): height, width = image.shape[:2] center_x, center_y = width // 2, height // 2 new_width = int(width * shrink_factor) new_height = int(height * shrink_factor) x1 = max(center_x - new_width // 2, 0) y1 = max(center_y - new_height // 2, 0) x2 = min(center_x + new_width // 2, width) y2 = min(center_y + new_height // 2, height) return image[y1:y2, x1:x2] def exclude_scaling_box(image: np.ndarray, bbox: np.ndarray, orig_size: tuple, processed_size: tuple, expansion_factor: float = 1.2) -> np.ndarray: x_min, y_min, x_max, y_max = map(int, bbox) scale_x = processed_size[1] / orig_size[1] scale_y = processed_size[0] / orig_size[0] x_min = int(x_min * scale_x) x_max = int(x_max * scale_x) y_min = int(y_min * scale_y) y_max = int(y_max * scale_y) box_width = x_max - x_min box_height = y_max - y_min expanded_x_min = max(0, int(x_min - (expansion_factor - 1) * box_width / 2)) expanded_x_max = min(image.shape[1], int(x_max + (expansion_factor - 1) * box_width / 2)) expanded_y_min = max(0, int(y_min - (expansion_factor - 1) * box_height / 2)) expanded_y_max = min(image.shape[0], int(y_max + (expansion_factor - 1) * box_height / 2)) image[expanded_y_min:expanded_y_max, expanded_x_min:expanded_x_max] = 0 return image import logging import time import signal import numpy as np import cv2 from scipy.interpolate import splprep, splev from scipy.ndimage import gaussian_filter1d from shapely.geometry import Point, Polygon import random import ezdxf import functools # Set up logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) # Custom TimeoutError class class TimeoutReachedError(Exception): pass # Timeout context manager class TimeoutContext: def __init__(self, seconds): self.seconds = seconds self.original_handler = None def timeout_handler(self, signum, frame): raise TimeoutReachedError(f"Function timed out after {self.seconds} seconds") def __enter__(self): if hasattr(signal, 'SIGALRM'): # Unix-like systems self.original_handler = signal.getsignal(signal.SIGALRM) signal.signal(signal.SIGALRM, self.timeout_handler) signal.alarm(self.seconds) self.start_time = time.time() return self def __exit__(self, exc_type, exc_val, exc_tb): if hasattr(signal, 'SIGALRM'): # Unix-like systems signal.alarm(0) signal.signal(signal.SIGALRM, self.original_handler) if exc_type is TimeoutReachedError: logger.warning(f"Timeout reached: {exc_val}") return True # Suppress the exception return False def resample_contour(contour): logger.info(f"Starting resample_contour with contour of shape {contour.shape}") num_points = 1000 smoothing_factor = 5 spline_degree = 3 if len(contour) < spline_degree + 1: error_msg = f"Contour must have at least {spline_degree + 1} 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}") tck, _ = splprep([contour[:, 0], contour[:, 1]], s=smoothing_factor) logger.debug("Generated spline parameters") u = np.linspace(0, 1, num_points) resampled_points = splev(u, tck) logger.debug(f"Resampled to {num_points} points") smoothed_x = gaussian_filter1d(resampled_points[0], sigma=1) smoothed_y = gaussian_filter1d(resampled_points[1], sigma=1) result = np.array([smoothed_x, smoothed_y]).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 extract_outlines(binary_image: np.ndarray) -> (np.ndarray, list): logger.info(f"Starting extract_outlines with image shape {binary_image.shape}") try: contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) logger.debug(f"Found {len(contours)} contours") outline_image = np.zeros_like(binary_image) cv2.drawContours(outline_image, contours, -1, (255), thickness=2) result_image = cv2.bitwise_not(outline_image) logger.info(f"Completed extract_outlines with {len(contours)} contours") return result_image, contours except Exception as e: logger.error(f"Error in extract_outlines: {e}") raise def union_tool_and_circle(tool_polygon: Polygon, center_inch, circle_diameter=1.0): logger.info(f"Starting union_tool_and_circle with center at {center_inch}") try: radius = circle_diameter / 2.0 circle_poly = Point(center_inch).buffer(radius, resolution=64) logger.debug(f"Created circle with radius {radius} at {center_inch}") union_poly = tool_polygon.union(circle_poly) logger.info(f"Completed union_tool_and_circle, result area: {union_poly.area}") return union_poly except Exception as e: logger.error(f"Error in union_tool_and_circle: {e}") raise def build_tool_polygon(points_inch): logger.info(f"Starting build_tool_polygon with {len(points_inch)} points") try: polygon = Polygon(points_inch) logger.info(f"Completed build_tool_polygon, polygon area: {polygon.area}") return polygon except Exception as e: logger.error(f"Error in build_tool_polygon: {e}") raise def polygon_to_exterior_coords(poly): logger.info(f"Starting polygon_to_exterior_coords with polygon type {poly.geom_type}") try: # Handle GeometryCollection case specifically if poly.geom_type == "GeometryCollection": logger.warning("Converting GeometryCollection to Polygon") # Find the largest geometry in the collection that has an exterior largest_area = 0 largest_geom = None for geom in poly.geoms: if hasattr(geom, 'area') and geom.area > largest_area: if hasattr(geom, 'exterior') or geom.geom_type == "MultiPolygon": largest_area = geom.area largest_geom = geom if largest_geom is None: logger.warning("No valid geometry found in GeometryCollection") return [] poly = largest_geom if poly.geom_type == "MultiPolygon": logger.debug("Converting MultiPolygon to single Polygon") biggest = max(poly.geoms, key=lambda g: g.area) poly = biggest if not hasattr(poly, 'exterior') or poly.exterior is None: logger.warning("Polygon has no exterior") return [] coords = list(poly.exterior.coords) logger.info(f"Completed polygon_to_exterior_coords with {len(coords)} coordinates") return coords except Exception as e: logger.error(f"Error in polygon_to_exterior_coords: {e}") # Return empty list as fallback return [] def place_finger_cut_adjusted( tool_polygon: Polygon, points_inch: list, existing_centers: list, all_polygons: list, circle_diameter: float = 1.0, min_gap: float = 0.5, max_attempts: int = 100 ) -> (Polygon, tuple): logger.info(f"Starting place_finger_cut_adjusted with {len(points_inch)} points") # Define fallback function for timeout case def fallback_solution(): logger.warning("Using fallback approach for finger cut placement") candidate_center = points_inch[len(points_inch) // 2] radius = circle_diameter / 2.0 candidate_circle = Point(candidate_center).buffer(radius, resolution=64) try: union_poly = tool_polygon.union(candidate_circle) except Exception as e: logger.warning(f"Fallback union failed, using buffer trick: {e}") union_poly = tool_polygon.buffer(0).union(candidate_circle.buffer(0)) existing_centers.append(candidate_center) logger.info(f"Used fallback finger cut at center {candidate_center}") return union_poly, candidate_center needed_center_distance = circle_diameter + min_gap radius = circle_diameter / 2.0 # Limit points to prevent timeout - use a subset for efficient processing if len(points_inch) > 100: logger.info(f"Limiting points from {len(points_inch)} to 100 for efficiency") step = len(points_inch) // 100 points_inch = points_inch[::step] # Randomize candidate points order indices = list(range(len(points_inch))) random.shuffle(indices) logger.debug(f"Shuffled {len(indices)} point indices") # Use a non-blocking timeout approach with explicit time checks start_time = time.time() timeout_seconds = 5 attempts = 0 try: while attempts < max_attempts: # Check if we're approaching the timeout current_time = time.time() if current_time - start_time > timeout_seconds - 0.1: # Leave 0.1s margin logger.warning(f"Approaching timeout after {attempts} attempts") return fallback_solution() # Process a batch of points to improve efficiency for i in indices: # Check timeout frequently if time.time() - start_time > timeout_seconds - 0.05: logger.warning("Timeout during point processing") return fallback_solution() cx, cy = points_inch[i] # Reduce the number of adjustments to speed up processing for dx, dy in [(0,0), (-0.2,0), (0.2,0), (0,0.2), (0,-0.2)]: candidate_center = (cx + dx, cy + dy) # Quick check for existing centers distance if any(np.hypot(candidate_center[0] - ex, candidate_center[1] - ey) < needed_center_distance for ex, ey in existing_centers): continue # Create candidate circle candidate_circle = Point(candidate_center).buffer(radius, resolution=32) # Reduced resolution # Quick geometric checks if tool_polygon.contains(candidate_circle) or not candidate_circle.intersects(tool_polygon): continue # Check intersection area - use simplified geometry for speed try: inter_area = candidate_circle.intersection(tool_polygon).area if inter_area <= 0 or inter_area >= candidate_circle.area: continue except Exception: continue # Quick distance check to other polygons too_close = False for other_poly in all_polygons: if other_poly.equals(tool_polygon): continue if other_poly.distance(candidate_circle) < min_gap: too_close = True break if too_close: continue # Attempt the union try: union_poly = tool_polygon.union(candidate_circle) # Check if we got a multi-polygon when we don't want one if union_poly.geom_type == "MultiPolygon" and len(union_poly.geoms) > 1: continue # Check if the union actually changed anything if union_poly.equals(tool_polygon): continue except Exception: continue # We found a valid candidate existing_centers.append(candidate_center) logger.info(f"Completed place_finger_cut_adjusted successfully at center {candidate_center}") return union_poly, candidate_center attempts += 1 # If we've made several attempts and are running out of time, use fallback if attempts >= max_attempts // 2 and (time.time() - start_time) > timeout_seconds * 0.8: logger.warning(f"Approaching timeout after {attempts} attempts") return fallback_solution() logger.debug(f"Completed attempt {attempts}/{max_attempts}") # If we reached max attempts without finding a solution logger.warning(f"No suitable finger cut found 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 save_dxf_spline(offset_value,inflated_contours, scaling_factor, height, finger_clearance=False): logger.info(f"Starting save_dxf_spline with {len(inflated_contours)} contours") degree = 3 closed = True try: doc = ezdxf.new(units=0) doc.units = ezdxf.units.IN doc.header["$INSUNITS"] = ezdxf.units.IN msp = doc.modelspace() finger_cut_centers = [] final_polygons_inch = [] for idx, contour in enumerate(inflated_contours): logger.debug(f"Processing contour {idx+1}/{len(inflated_contours)}") try: resampled_contour = resample_contour(contour) points_inch = [(x * scaling_factor, (height - y) * scaling_factor) for x, y in resampled_contour] if len(points_inch) < 3: logger.warning(f"Skipping contour {idx}: insufficient points ({len(points_inch)})") continue if np.linalg.norm(np.array(points_inch[0]) - np.array(points_inch[-1])) > 1e-2: logger.debug("Closing contour by adding first point to end") points_inch.append(points_inch[0]) tool_polygon = build_tool_polygon(points_inch) if finger_clearance: logger.debug("Applying finger clearance") try: # Use a hard 5-second timeout for the entire finger cut operation start_time = time.time() union_poly, center = place_finger_cut_adjusted( tool_polygon, points_inch, finger_cut_centers, final_polygons_inch, circle_diameter=1.0, min_gap=(0.5+offset_value), max_attempts=100 ) # Check if we exceeded the timeout anyway if time.time() - start_time > 5: logger.warning(f"Finger cut took too long for contour {idx} ({time.time() - start_time:.2f}s)") if union_poly is not None: tool_polygon = union_poly logger.debug(f"Applied finger cut at {center}") except Exception as e: logger.warning(f"Finger cut failed for contour {idx}: {e}, using original polygon") exterior_coords = polygon_to_exterior_coords(tool_polygon) if len(exterior_coords) < 3: logger.warning(f"Skipping contour {idx}: insufficient exterior points ({len(exterior_coords)})") continue for existing_poly in final_polygons_inch: if tool_polygon.intersects(existing_poly): # Check if the intersection is more than just touching points intersection = tool_polygon.intersection(existing_poly) # If the intersection has ANY area (not just points touching) if intersection.area > 0: # Zero tolerance for overlap logger.error(f"Polygon {idx} overlaps with an existing polygon") raise FingerCutOverlapError("There was an overlap with fingercuts... Please try again to generate dxf.") msp.add_spline(exterior_coords, degree=degree, dxfattribs={"layer": "TOOLS"}) final_polygons_inch.append(tool_polygon) logger.debug(f"Added spline for contour {idx}") except ValueError as e: logger.warning(f"Skipping contour {idx}: {e}") logger.info(f"Completed save_dxf_spline with {len(final_polygons_inch)} successful polygons") return doc, final_polygons_inch except Exception as e: logger.error(f"Error in save_dxf_spline: {e}") raise def add_rectangular_boundary(doc, polygons_inch, boundary_length, boundary_width, offset_unit, annotation_text="", image_height_in=None, image_width_in=None): msp = doc.modelspace() # Convert from mm if necessary if offset_unit.lower() == "mm": if boundary_length < 50: boundary_length = boundary_length * 25.4 if boundary_width < 50: boundary_width = boundary_width * 25.4 boundary_length_in = boundary_length / 25.4 boundary_width_in = boundary_width / 25.4 else: boundary_length_in = boundary_length boundary_width_in = boundary_width # Compute bounding box of inner contours min_x = float("inf") min_y = float("inf") max_x = -float("inf") max_y = -float("inf") for poly in polygons_inch: b = poly.bounds min_x = min(min_x, b[0]) min_y = min(min_y, b[1]) max_x = max(max_x, b[2]) max_y = max(max_y, b[3]) if min_x == float("inf"): print("No tool polygons found, skipping boundary.") return None # Compute inner bounding box dimensions inner_width = max_x - min_x inner_length = max_y - min_y # Set clearance margins clearance_side = 0.25 # left/right clearance clearance_tb = 0.25 # top/bottom clearance if annotation_text.strip(): clearance_tb = 0.75 # Calculate center of inner contours center_x = (min_x + max_x) / 2 center_y = (min_y + max_y) / 2 # Draw rectangle centered at (center_x, center_y) left = center_x - boundary_width_in / 2 right = center_x + boundary_width_in / 2 bottom = center_y - boundary_length_in / 2 top = center_y + boundary_length_in / 2 rect_coords = [(left, bottom), (right, bottom), (right, top), (left, top), (left, bottom)] from shapely.geometry import Polygon boundary_polygon = Polygon(rect_coords) msp.add_lwpolyline(rect_coords, close=True, dxfattribs={"layer": "BOUNDARY"}) text_top = boundary_polygon.bounds[1] + 1 too_small = boundary_width_in < inner_width + 2 * clearance_side or boundary_length_in < inner_length + 2 * clearance_tb if too_small: raise BoundaryOverlapError("Error: The specified boundary dimensions are too small and overlap with the inner contours. Please provide larger value for boundary length and width.") if annotation_text.strip() and text_top > min_y - 1: raise TextOverlapError("Error: The text is too close to the inner contours. Please provide larger value for boundary length and width.") return boundary_polygon def draw_polygons_inch(polygons_inch, image_rgb, scaling_factor, image_height, color=(0,0,255), thickness=2): for poly in polygons_inch: if poly.geom_type == "MultiPolygon": for subpoly in poly.geoms: draw_single_polygon(subpoly, image_rgb, scaling_factor, image_height, color, thickness) else: draw_single_polygon(poly, image_rgb, scaling_factor, image_height, color, thickness) def draw_single_polygon(poly, image_rgb, scaling_factor, image_height, color=(0,0,255), thickness=2): ext = list(poly.exterior.coords) if len(ext) < 3: return pts_px = [] for (x_in, y_in) in ext: px = int(x_in / scaling_factor) py = int(image_height - (y_in / scaling_factor)) pts_px.append([px, py]) pts_px = np.array(pts_px, dtype=np.int32) cv2.polylines(image_rgb, [pts_px], isClosed=True, color=color, thickness=thickness, lineType=cv2.LINE_AA) # def draw_and_pad(polygons_inch, scaling_factor,boundary_polygon, padding=50, # color=(0,0,255), thickness=2): # """ # - polygons_inch: list of Shapely Polygons in inch units (already including boundary). # - scaling_factor: inches per pixel. # - padding: padding in pixels. # """ # all_x = [] # all_y = [] # pixel_polys = [] # # 1) Convert to pixel coords and collect bounds # for poly in polygons_inch: # coords = list(poly.exterior.coords) # pts = [] # for x_in, y_in in coords: # px = int(round(x_in / scaling_factor)) # py = int(round(y_in / scaling_factor)) # pts.append([px, py]) # all_x.append(px) # all_y.append(py) # pixel_polys.append(np.array(pts, dtype=np.int32)) # # 2) Compute canvas size # min_x, max_x = min(all_x), max(all_x) # min_y, max_y = min(all_y), max(all_y) # width = max_x - min_x + 1 # height = max_y - min_y + 1 # # 3) Create blank white canvas # canvas = 255 * np.ones((height, width, 3), dtype=np.uint8) # # 4) Draw each polygon, flipping y within the local box # for pts in pixel_polys: # # Offset so min corner is (0,0) # pts_off = pts - np.array([[min_x, min_y]]) # # Flip y: new_y = height-1 - old_y # pts_off[:,1] = (height - 1) - pts_off[:,1] # cv2.polylines(canvas, [pts_off], isClosed=True, # color=color, thickness=thickness, lineType=cv2.LINE_AA) # # 5) Pad the canvas # padded = cv2.copyMakeBorder( # canvas, # top=padding, bottom=padding, # left=padding, right=padding, # borderType=cv2.BORDER_CONSTANT, # value=[255,255,255] # ) # return padded # import numpy as np # import cv2 # def draw_and_pad(polygons_inch, scaling_factor, boundary_polygon, padding=50, # color=(0,0,255), thickness=2): # """ # - polygons_inch: list of Shapely Polygons in inch units. # - scaling_factor: inches per pixel. # - boundary_polygon: the Shapely boundary polygon, or None. # - padding: base padding in pixels. # """ # all_x, all_y = [], [] # pixel_polys = [] # # 1) Convert to pixel coords and collect bounds # for poly in polygons_inch: # coords = list(poly.exterior.coords) # pts = [] # for x_in, y_in in coords: # px = int(round(x_in / scaling_factor)) # py = int(round(y_in / scaling_factor)) # pts.append([px, py]) # all_x.append(px) # all_y.append(py) # pixel_polys.append(np.array(pts, dtype=np.int32)) # # 2) Compute canvas size # min_x, max_x = min(all_x), max(all_x) # min_y, max_y = min(all_y), max(all_y) # width = max_x - min_x + 1 # height = max_y - min_y + 1 # # 3) Create blank white canvas # canvas = 255 * np.ones((height, width, 3), dtype=np.uint8) # # 4) Draw each polygon, flipping y within the local box # for pts in pixel_polys: # pts_off = pts - np.array([[min_x, min_y]]) # pts_off[:,1] = (height - 1) - pts_off[:,1] # cv2.polylines(canvas, [pts_off], isClosed=True, # color=color, thickness=thickness, lineType=cv2.LINE_AA) # # 5) Decide padding amounts # if boundary_polygon is not None: # top = bottom = left = right = padding # else: # # Double the padding if there's no boundary, to avoid clipping # top = bottom = left = right = padding * 2 # # 6) Pad the canvas # padded = cv2.copyMakeBorder( # canvas, # top=top, bottom=bottom, # left=left, right=right, # borderType=cv2.BORDER_CONSTANT, # value=[255,255,255] # ) # return padded import numpy as np import cv2 # def draw_and_pad(polygons_inch, scaling_factor, boundary_polygon, padding=50, # color=(0, 0, 255), thickness=2): # """ # Draws Shapely Polygons (in inch units) on a white canvas. # When boundary_polygon is None, the computed bounds are expanded by the padding value # so that the drawn contours are not clipped at the edges after adding the final padding. # Arguments: # polygons_inch: list of Shapely Polygons in inch units (already including boundary). # scaling_factor: inches per pixel. # boundary_polygon: the Shapely boundary polygon, or None. # padding: padding in pixels. # color: color of the drawn polylines (in BGR format). # thickness: line thickness. # Returns: # padded: an image (numpy array) of the drawn polygons with an external white border. # """ # all_x = [] # all_y = [] # pixel_polys = [] # # 1) Convert each polygon to pixel coordinates and compute overall bounds. # for poly in polygons_inch: # coords = list(poly.exterior.coords) # pts = [] # for x_in, y_in in coords: # px = int(round(x_in / scaling_factor)) # py = int(round(y_in / scaling_factor)) # pts.append([px, py]) # all_x.append(px) # all_y.append(py) # pixel_polys.append(np.array(pts, dtype=np.int32)) # # 2) Compute the basic canvas size from the polygon bounds. # min_x, max_x = min(all_x), max(all_x) # min_y, max_y = min(all_y), max(all_y) # # If no boundary polygon is provided, expand the bounds to add margin # # so that later when we pad externally, the contours do not get clipped. # if boundary_polygon is None: # min_x -= padding # max_x += padding # min_y -= padding # max_y += padding # width = max_x - min_x + 1 # height = max_y - min_y + 1 # # 3) Create a blank white canvas. # canvas = 255 * np.ones((height, width, 3), dtype=np.uint8) # # 4) Draw each polygon, flipping the y-coordinates to match image coordinates. # for pts in pixel_polys: # # Offset so the minimum corner becomes (0,0) on canvas. # pts_off = pts - np.array([[min_x, min_y]]) # # Flip y: image coordinates have (0,0) at the top-left. # pts_off[:, 1] = (height - 1) - pts_off[:, 1] # cv2.polylines(canvas, [pts_off], isClosed=True, # color=color, thickness=thickness, lineType=cv2.LINE_AA) # # 5) Finally, add external padding on all sides. # padded = cv2.copyMakeBorder( # canvas, # top=padding, bottom=padding, # left=padding, right=padding, # borderType=cv2.BORDER_CONSTANT, # value=[255, 255, 255] # ) # return padded import numpy as np import cv2 from shapely.geometry import Polygon import numpy as np import cv2 from shapely.geometry import Polygon def draw_and_pad(polygons_inch, scaling_factor, # inches per pixel boundary_polygon=None, max_res=1024, simplify_tol_px=1.0, padding_px=50, color=(0,0,255), thickness=2): # 1) Simplify & collect raw coords in inches all_x, all_y = [], [] simple_polys = [] for poly in polygons_inch: tol_in = simplify_tol_px * scaling_factor / max_res simp = poly.simplify(tolerance=tol_in, preserve_topology=True) coords = np.array(simp.exterior.coords) # (N,2) in inches all_x.extend(coords[:,0]) all_y.extend(coords[:,1]) simple_polys.append(coords) # 2) Compute full‑res pixel extents min_x_in, max_x_in = min(all_x), max(all_x) min_y_in, max_y_in = min(all_y), max(all_y) w_in = (max_x_in - min_x_in) if boundary_polygon is None else (max_x_in - min_x_in) h_in = (max_y_in - min_y_in) if boundary_polygon is None else (max_y_in - min_y_in) full_w_px = np.ceil(w_in / scaling_factor) full_h_px = np.ceil(h_in / scaling_factor) # 3) Compute preview scale ≤1 so dims ≤ max_res scale = min(max_res / full_w_px, max_res / full_h_px, 1.0) # 4) Compute preview dims & allocate _fully‐padded_ canvas W = int(np.ceil(full_w_px * scale)) H = int(np.ceil(full_h_px * scale)) PW, PH = W + 2*padding_px, H + 2*padding_px canvas = 255 * np.ones((PH, PW, 3), dtype=np.uint8) # Precompute offsets (in preview px) of the “world origin” off_x = int(np.floor(min_x_in / scaling_factor * scale)) off_y = int(np.floor(min_y_in / scaling_factor * scale)) # 5) Draw each polygon, now fully inside the padded canvas for coords in simple_polys: # inch→preview‐px transform pts = ((coords / scaling_factor) * scale).round().astype(int) # shift by both the minimum and the padding: pts[:,0] = pts[:,0] - off_x + padding_px pts[:,1] = pts[:,1] - off_y + padding_px # flip Y into image coords pts[:,1] = PH - 1 - pts[:,1] cv2.polylines(canvas, [pts], isClosed=True, color=color, thickness=thickness, lineType=cv2.LINE_AA) return canvas, scale, off_y, padding_px, PH # --------------------- # Main Predict Function with Finger Cut Clearance, Boundary Box, Annotation and Sharpness Enhancement # --------------------- def predict( image: Union[str, bytes, np.ndarray], offset_value: float, offset_unit: str, # "mm" or "inches" finger_clearance: str, # "Yes" or "No" add_boundary: str, # "Yes" or "No" boundary_length: float, boundary_width: float, annotation_text: str ): overall_start = time.time() # Convert image to NumPy array if needed if isinstance(image, str): if os.path.exists(image): image = np.array(Image.open(image).convert("RGB")) else: try: image = np.array(Image.open(io.BytesIO(base64.b64decode(image))).convert("RGB")) except Exception: raise ValueError("Invalid base64 image data") # Apply brightness and sharpness enhancement if isinstance(image, np.ndarray): pil_image = Image.fromarray(image) enhanced_image = ImageEnhance.Sharpness(pil_image).enhance(1.5) image = np.array(enhanced_image) # --------------------- # 1) Detect the drawer with YOLOWorld (or use original image if not detected) # --------------------- drawer_detected = True try: t = time.time() drawer_img = yolo_detect(image) print("Drawer detection completed in {:.2f} seconds".format(time.time() - t)) except DrawerNotDetectedError as e: print(f"Drawer not detected: {e}, using original image.") drawer_detected = False drawer_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # Process the image (either cropped drawer or original) t = time.time() if drawer_detected: # For detected drawers: shrink and square shrunked_img = make_square(shrink_bbox(drawer_img, 0.95)) else: # For non-drawer images: keep original dimensions shrunked_img = drawer_img # Already in BGR format from above del drawer_img gc.collect() print("Image processing completed in {:.2f} seconds".format(time.time() - t)) # --------------------- # 2) Detect the reference box with YOLO (now works on either cropped or original image) # --------------------- try: t = time.time() reference_obj_img, scaling_box_coords = detect_reference_square(shrunked_img) print("Reference coin detection completed in {:.2f} seconds".format(time.time() - t)) except ReferenceBoxNotDetectedError as e: return None, None, None, None, f"Error: {str(e)}" # --------------------- # 3) Remove background of the reference box to compute scaling factor # --------------------- t = time.time() reference_obj_img = make_square(reference_obj_img) reference_square_mask = remove_bg_u2netp(reference_obj_img) reference_square_mask= resize_img(reference_square_mask,(reference_obj_img.shape[1],reference_obj_img.shape[0])) print("Reference image processing completed in {:.2f} seconds".format(time.time() - t)) t = time.time() try: cv2.imwrite("mask.jpg", cv2.cvtColor(reference_obj_img, cv2.COLOR_RGB2GRAY)) scaling_factor = calculate_scaling_factor( target_image=reference_square_mask, reference_obj_size_mm=0.955, feature_detector="ORB", ) except ZeroDivisionError: scaling_factor = None print("Error calculating scaling factor: Division by zero") except Exception as e: scaling_factor = None print(f"Error calculating scaling factor: {e}") if scaling_factor is None or scaling_factor == 0: scaling_factor = 0.7 print("Using default scaling factor of 0.7 due to calculation error") gc.collect() print("Scaling factor determined: {}".format(scaling_factor)) # --------------------- # 4) Optional boundary dimension checks (now without size limits) # --------------------- if add_boundary.lower() == "yes": if offset_unit.lower() == "mm": if boundary_length < 50: boundary_length = boundary_length * 25.4 if boundary_width < 50: boundary_width = boundary_width * 25.4 boundary_length_in = boundary_length / 25.4 boundary_width_in = boundary_width / 25.4 else: boundary_length_in = boundary_length boundary_width_in = boundary_width # --------------------- # 5) Remove background from the shrunked drawer image (main objects) # --------------------- if offset_unit.lower() == "mm": if offset_value < 1: offset_value = offset_value * 25.4 offset_inches = offset_value / 25.4 if offset_value==0: offset_value = offset_value * 25.4 offset_inches = offset_value / 25.4 offset_inches+=0.005 else: offset_inches = offset_value if offset_inches==0: offset_inches+=0.005 t = time.time() orig_size = shrunked_img.shape[:2] objects_mask = remove_bg(shrunked_img) processed_size = objects_mask.shape[:2] objects_mask = exclude_scaling_box(objects_mask, scaling_box_coords, orig_size, processed_size, expansion_factor=1.2) objects_mask = resize_img(objects_mask, (shrunked_img.shape[1], shrunked_img.shape[0])) del scaling_box_coords gc.collect() print("Object masking completed in {:.2f} seconds".format(time.time() - t)) # Dilate mask by offset_pixels t = time.time() offset_pixels = (offset_inches / scaling_factor) * 2 + 1 if scaling_factor != 0 else 1 dilated_mask = cv2.dilate(objects_mask, np.ones((int(offset_pixels), int(offset_pixels)), np.uint8)) del objects_mask gc.collect() print("Mask dilation completed in {:.2f} seconds".format(time.time() - t)) Image.fromarray(dilated_mask).save("./outputs/scaled_mask_new.jpg") # --------------------- # 6) Extract outlines from the mask and convert them to DXF splines # --------------------- t = time.time() outlines, contours = extract_outlines(dilated_mask) print("Outline extraction completed in {:.2f} seconds".format(time.time() - t)) output_img = shrunked_img.copy() del shrunked_img gc.collect() t = time.time() use_finger_clearance = True if finger_clearance.lower() == "yes" else False doc, final_polygons_inch = save_dxf_spline( offset_inches,contours, scaling_factor, processed_size[0], finger_clearance=use_finger_clearance ) del contours gc.collect() print("DXF generation completed in {:.2f} seconds".format(time.time() - t)) # --------------------- # Compute bounding box of inner tool contours BEFORE adding optional boundary # --------------------- inner_min_x = float("inf") inner_min_y = float("inf") inner_max_x = -float("inf") inner_max_y = -float("inf") for poly in final_polygons_inch: b = poly.bounds inner_min_x = min(inner_min_x, b[0]) inner_min_y = min(inner_min_y, b[1]) inner_max_x = max(inner_max_x, b[2]) inner_max_y = max(inner_max_y, b[3]) # --------------------- # 7) Add optional rectangular boundary # --------------------- boundary_polygon = None if add_boundary.lower() == "yes": boundary_polygon = add_rectangular_boundary( doc, final_polygons_inch, boundary_length, boundary_width, offset_unit, annotation_text, image_height_in=output_img.shape[0] * scaling_factor, image_width_in=output_img.shape[1] * scaling_factor ) if boundary_polygon is not None: final_polygons_inch.append(boundary_polygon) # --------------------- # 8) Add annotation text (if provided) in the DXF # --------------------- msp = doc.modelspace() if annotation_text.strip(): if boundary_polygon is not None: text_height_dxf = 0.75 text_y_dxf = boundary_polygon.bounds[1] + 0.25 font = get_font_face("Arial") # Create text paths first paths = text2path.make_paths_from_str( annotation_text.strip().upper(), font=font, size=text_height_dxf, align=TextEntityAlignment.LEFT ) # Calculate actual text width from the path's bounds text_bbox = path.bbox(paths) #text_width = text_bbox[2] - text_bbox[0] # xmax - xmin #text_width = text_bbox.width # Calculate center point of inner tool contours center_x = (inner_min_x + inner_max_x) / 2.0 text_width = text_bbox.extmax.x - text_bbox.extmin.x # Calculate starting x position for truly centered text text_x = center_x - (text_width / 2.0) # Create a translation matrix translation = ezdxf.math.Matrix44.translate(text_x, text_y_dxf, 0) # Apply the translation to each path translated_paths = [p.transform(translation) for p in paths] # Render the paths as splines and polylines path.render_splines_and_polylines( msp, translated_paths, dxfattribs={"layer": "ANNOTATION", "color": 7} ) # Save the DXF dxf_filepath = os.path.join("./outputs", "out.dxf") doc.saveas(dxf_filepath) # --------------------- # 9) For the preview images, draw the polygons and place text similarly # --------------------- #draw_polygons_inch(final_polygons_inch, output_img, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2) for poly in final_polygons_inch: # Skip the boundary polygon if boundary_polygon is not None and poly == boundary_polygon: continue draw_single_polygon(poly, output_img, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2) new_outlines,preview_scale, off_y, padding_px, PH= draw_and_pad(final_polygons_inch, scaling_factor,boundary_polygon) #draw_polygons_inch(final_polygons_inch, new_outlines, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2) import math if annotation_text.strip(): # Common variables font = cv2.FONT_HERSHEY_SIMPLEX text = annotation_text.strip().upper() canvas_height, canvas_width = new_outlines.shape[:2] if boundary_polygon is not None: # Keep original code for output_img text_x_img = int(((inner_min_x + inner_max_x) / 2.0) / scaling_factor) text_y_in = boundary_polygon.bounds[1] + 0.25 text_y_img = int(processed_size[0] - (text_y_in / scaling_factor)) org = (text_x_img - int(len(annotation_text.strip()) * 6), text_y_img) # Process for output_img with mask (keeping your original code) temp_img = np.zeros_like(output_img) cv2.putText(temp_img, text, org, font, 2, (0, 0, 255), 4, cv2.LINE_AA) cv2.putText(temp_img, text, org, font, 2, (255, 255, 255), 2, cv2.LINE_AA) outline_mask = cv2.cvtColor(temp_img, cv2.COLOR_BGR2GRAY) _, outline_mask = cv2.threshold(outline_mask, 1, 255, cv2.THRESH_BINARY) output_img[outline_mask > 0] = temp_img[outline_mask > 0] # For new_outlines - simple, centered text def optimal_font_dims(img, font_scale = 1e-3, thickness_scale = 2e-3): h, w, _ = img.shape font_scale = min(w, h) * font_scale thickness = math.ceil(min(w, h) * thickness_scale) return font_scale, thickness font_scale,thickness = optimal_font_dims(new_outlines) (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness) text_x = (canvas_width - text_width) // 2 raw_y = (text_y_in / scaling_factor) * preview_scale y1 = raw_y - off_y + padding_px text_y_px = int(round(PH - 1 - y1)) text_y_px_adjusted = text_y_px - baseline # bottom_margin_px = int(0.25 / scaling_factor) # font_scale,_ = optimal_font_dims(new_outlines) #text_y_outlines = int(canvas_height - (text_y_in + (0.75) / scaling_factor)) # First outline, then inner text cv2.putText(new_outlines, text, (text_x, text_y_px_adjusted), font, font_scale, (0, 0, 255), thickness+2, cv2.LINE_AA) cv2.putText(new_outlines, text, (text_x, text_y_px_adjusted), font, font_scale, (255, 255, 255), thickness-1, cv2.LINE_AA) outlines_color = cv2.cvtColor(new_outlines, cv2.COLOR_BGR2RGB) print("Total prediction time: {:.2f} seconds".format(time.time() - overall_start)) return ( cv2.cvtColor(output_img, cv2.COLOR_BGR2RGB), outlines_color, dxf_filepath, dilated_mask, str(scaling_factor) ) # --------------------- # Gradio Interface # --------------------- if __name__ == "__main__": os.makedirs("./outputs", exist_ok=True) def gradio_predict(img, offset, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text): try: return predict(img, offset, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text) except Exception as e: return None, None, None, None, f"Error: {str(e)}" iface = gr.Interface( fn=gradio_predict, inputs=[ gr.Image(label="Input Image"), gr.Number(label="Offset value for Mask", value=0.075), gr.Dropdown(label="Offset Unit", choices=["mm", "inches"], value="inches"), gr.Dropdown(label="Add Finger Clearance?", choices=["Yes", "No"], value="Yes"), gr.Dropdown(label="Add Rectangular Boundary?", choices=["Yes", "No"], value="Yes"), gr.Number(label="Boundary Length", value=50, precision=2), gr.Number(label="Boundary Width", value=50, precision=2), gr.Textbox(label="Annotation (max 20 chars)", max_length=20, placeholder="Type up to 20 characters") ], outputs=[ gr.Image(format="png",label="Output Image"), gr.Image(format="png",label="Outlines of Objects"), gr.File(label="DXF file"), gr.Image(label="Mask"), gr.Textbox(label="Scaling Factor (inches/pixel)") ], examples=[ ["./Test20.jpg", 0.075, "inches", "Yes", "No", 300.0, 200.0, "MyTool"], ["./Test21.jpg", 0.075, "inches", "Yes", "Yes", 300.0, 200.0, "Tool2"] ] ) iface.launch(share=True)