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): """Raised when the optional boundary dimensions are too small and overlap with the inner contours.""" pass class TextOverlapError(Exception): """Raised when the text overlaps with the inner contours (with a margin of 0.75).""" 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=0.3) if not res or len(res) == 0 or len(res[0].boxes) == 0: raise ReferenceBoxNotDetectedError("Reference Coin not detected in the image.") print("Reference 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 def resample_contour(contour): num_points = 1000 smoothing_factor = 5 spline_degree = 3 if len(contour) < spline_degree + 1: raise ValueError(f"Contour must have at least {spline_degree + 1} points, but has {len(contour)} points.") contour = contour[:, 0, :] tck, _ = splprep([contour[:, 0], contour[:, 1]], s=smoothing_factor) u = np.linspace(0, 1, num_points) resampled_points = splev(u, tck) smoothed_x = gaussian_filter1d(resampled_points[0], sigma=1) smoothed_y = gaussian_filter1d(resampled_points[1], sigma=1) return np.array([smoothed_x, smoothed_y]).T # --------------------- # Add the missing extract_outlines function # --------------------- def extract_outlines(binary_image: np.ndarray) -> (np.ndarray, list): contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) outline_image = np.zeros_like(binary_image) cv2.drawContours(outline_image, contours, -1, (255), thickness=2) return cv2.bitwise_not(outline_image), contours # --------------------- # Functions for Finger Cut Clearance # --------------------- def union_tool_and_circle(tool_polygon: Polygon, center_inch, circle_diameter=1.0): radius = circle_diameter / 2.0 circle_poly = Point(center_inch).buffer(radius, resolution=64) union_poly = tool_polygon.union(circle_poly) return union_poly def build_tool_polygon(points_inch): return Polygon(points_inch) def polygon_to_exterior_coords(poly: Polygon): if poly.geom_type == "MultiPolygon": biggest = max(poly.geoms, key=lambda g: g.area) poly = biggest if not poly.exterior: return [] return list(poly.exterior.coords) def place_finger_cut_adjusted(tool_polygon, points_inch, existing_centers, all_polygons, circle_diameter=1.0, min_gap=0.25, max_attempts=30): #1st best needed_center_distance = circle_diameter + min_gap radius = circle_diameter / 2.0 import random for _ in range(max_attempts): idx = random.randint(0, len(points_inch) - 1) cx, cy = points_inch[idx] # Check if this point is too close to an existing center too_close = any(np.hypot(cx - ex_x, cy - ex_y) < needed_center_distance for ex_x, ex_y in existing_centers) if too_close: continue # Create the finger cut circle and try adding it to the tool circle_poly = Point((cx, cy)).buffer(radius, resolution=64) union_poly = tool_polygon.union(circle_poly) # Check for overlap and spacing with other tools overlap_with_others = False too_close_to_others = False for poly in all_polygons: if poly.equals(tool_polygon): continue # Skip comparing the tool to itself if union_poly.intersects(poly): overlap_with_others = True break if circle_poly.buffer(min_gap).intersects(poly): too_close_to_others = True break if overlap_with_others or too_close_to_others: continue existing_centers.append((cx, cy)) return union_poly, (cx, cy) print("Warning: Could not place a finger cut circle meeting all spacing requirements.") return None, None # --------------------- # DXF Spline and Boundary Functions # --------------------- def save_dxf_spline(inflated_contours, scaling_factor, height, finger_clearance=False): degree = 3 closed = True 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 contour in 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: continue if np.linalg.norm(np.array(points_inch[0]) - np.array(points_inch[-1])) > 1e-2: points_inch.append(points_inch[0]) tool_polygon = build_tool_polygon(points_inch) if finger_clearance: union_poly, center = place_finger_cut_adjusted(tool_polygon, points_inch, finger_cut_centers, final_polygons_inch, circle_diameter=1.0, min_gap=0.25, max_attempts=100) if union_poly is not None: tool_polygon = union_poly exterior_coords = polygon_to_exterior_coords(tool_polygon) if len(exterior_coords) < 3: continue msp.add_spline(exterior_coords, degree=degree, dxfattribs={"layer": "TOOLS"}) final_polygons_inch.append(tool_polygon) except ValueError as e: print(f"Skipping contour: {e}") return doc, final_polygons_inch 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 as ShapelyPolygon boundary_polygon = ShapelyPolygon(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 values.") if annotation_text.strip() and text_top > min_y - 0.75: raise TextOverlapError("Error: The text is too close to the inner contours. Please increase boundary length.") 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) # --------------------- # 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, 0.90)) 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 else: offset_inches = offset_value 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( 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(): text_x = ((inner_min_x + inner_max_x) / 2.0) - (int(len(annotation_text.strip()) / 2.0)) text_height_dxf = 0.75 text_y_dxf = boundary_polygon.bounds[1] + 0.25 font = get_font_face("Arial") paths = text2path.make_paths_from_str( annotation_text.strip().upper(), font=font, # Use default font size=text_height_dxf, align=TextEntityAlignment.LEFT ) # 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) new_outlines = np.ones_like(output_img) * 255 draw_polygons_inch(final_polygons_inch, new_outlines, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2) if annotation_text.strip(): text_height_cv = 0.75 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) # Method 2: Use two different thicknesses # Draw thicker outline temp_img = np.zeros_like(output_img) cv2.putText( temp_img, annotation_text.strip().upper(), org, cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), # Red color 4, # Thicker outline cv2.LINE_AA ) cv2.putText( temp_img, annotation_text.strip().upper(), org, cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), # Black to create hole 2, # Thinner inner part 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] cv2.putText( new_outlines, annotation_text.strip().upper(), org, cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), # Red color 4, # Thicker outline cv2.LINE_AA ) cv2.putText( new_outlines, annotation_text.strip().upper(), org, cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), # Inner text in white 2, # Thinner inner part 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="No"), gr.Dropdown(label="Add Rectangular Boundary?", choices=["Yes", "No"], value="No"), gr.Number(label="Boundary Length", value=300.0, precision=2), gr.Number(label="Boundary Width", value=200.0, precision=2), gr.Textbox(label="Annotation (max 20 chars)", max_length=20, placeholder="Type up to 20 characters") ], outputs=[ gr.Image(label="Output Image"), gr.Image(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", "No", "No", 30.0, 20.0, "MyTool"], ["./Test21.jpg", 0.075, "inches", "Yes", "Yes", 30.0, 20.0, "Tool2"] ] ) iface.launch(share=True)