from torchvision.models.detection import keypointrcnn_resnet50_fpn from torchvision.models.detection.faster_rcnn import FastRCNNPredictor from torchvision.models.detection.keypoint_rcnn import KeypointRCNNPredictor from torchvision.models.detection import KeypointRCNN_ResNet50_FPN_Weights import random import torch from torch.utils.data import Dataset import torchvision.transforms.functional as F import numpy as np from torch.utils.data.dataloader import default_collate import cv2 import matplotlib.pyplot as plt from torch.utils.data import DataLoader, Subset, ConcatDataset from tqdm import tqdm from torch.optim import SGD import time from torch.optim import AdamW import copy from torchvision import transforms object_dict = { 0: 'background', 1: 'task', 2: 'exclusiveGateway', 3: 'event', 4: 'parallelGateway', 5: 'messageEvent', 6: 'pool', 7: 'lane', 8: 'dataObject', 9: 'dataStore', 10: 'subProcess', 11: 'eventBasedGateway', 12: 'timerEvent', } arrow_dict = { 0: 'background', 1: 'sequenceFlow', 2: 'dataAssociation', 3: 'messageFlow', } class_dict = { 0: 'background', 1: 'task', 2: 'exclusiveGateway', 3: 'event', 4: 'parallelGateway', 5: 'messageEvent', 6: 'pool', 7: 'lane', 8: 'dataObject', 9: 'dataStore', 10: 'subProcess', 11: 'eventBasedGateway', 12: 'timerEvent', 13: 'sequenceFlow', 14: 'dataAssociation', 15: 'messageFlow', } def rescale_boxes(scale, boxes): for i in range(len(boxes)): boxes[i] = [boxes[i][0]*scale, boxes[i][1]*scale, boxes[i][2]*scale, boxes[i][3]*scale] return boxes def iou(box1, box2): # Calcule l'intersection des deux boîtes englobantes inter_box = [max(box1[0], box2[0]), max(box1[1], box2[1]), min(box1[2], box2[2]), min(box1[3], box2[3])] inter_area = max(0, inter_box[2] - inter_box[0]) * max(0, inter_box[3] - inter_box[1]) # Calcule l'union des deux boîtes englobantes box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1]) union_area = box1_area + box2_area - inter_area return inter_area / union_area def proportion_inside(box1, box2): # Calculate the intersection of the two bounding boxes inter_box = [max(box1[0], box2[0]), max(box1[1], box2[1]), min(box1[2], box2[2]), min(box1[3], box2[3])] inter_area = max(0, inter_box[2] - inter_box[0]) * max(0, inter_box[3] - inter_box[1]) # Calculate the area of box1 box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) # Calculate the proportion of box1 inside box2 if box1_area == 0: return 0 proportion = inter_area / box1_area # Ensure the proportion is at most 100% return min(proportion, 1.0) def resize_boxes(boxes, original_size, target_size): """ Resizes bounding boxes according to a new image size. Parameters: - boxes (np.array): The original bounding boxes as a numpy array of shape [N, 4]. - original_size (tuple): The original size of the image as (width, height). - target_size (tuple): The desired size to resize the image to as (width, height). Returns: - np.array: The resized bounding boxes as a numpy array of shape [N, 4]. """ orig_width, orig_height = original_size target_width, target_height = target_size # Calculate the ratios for width and height width_ratio = target_width / orig_width height_ratio = target_height / orig_height # Apply the ratios to the bounding boxes boxes[:, 0] *= width_ratio boxes[:, 1] *= height_ratio boxes[:, 2] *= width_ratio boxes[:, 3] *= height_ratio return boxes def resize_keypoints(keypoints: np.ndarray, original_size: tuple, target_size: tuple) -> np.ndarray: """ Resize keypoints based on the original and target dimensions of an image. Parameters: - keypoints (np.ndarray): The array of keypoints, where each keypoint is represented by its (x, y) coordinates. - original_size (tuple): The width and height of the original image (width, height). - target_size (tuple): The width and height of the target image (width, height). Returns: - np.ndarray: The resized keypoints. Explanation: The function calculates the ratio of the target dimensions to the original dimensions. It then applies these ratios to the x and y coordinates of each keypoint to scale them appropriately to the target image size. """ orig_width, orig_height = original_size target_width, target_height = target_size # Calculate the ratios for width and height scaling width_ratio = target_width / orig_width height_ratio = target_height / orig_height # Apply the scaling ratios to the x and y coordinates of each keypoint keypoints[:, 0] *= width_ratio # Scale x coordinates keypoints[:, 1] *= height_ratio # Scale y coordinates return keypoints class RandomCrop: def __init__(self, new_size=(1333,800),crop_fraction=0.5, min_objects=4): self.crop_fraction = crop_fraction self.min_objects = min_objects self.new_size = new_size def __call__(self, image, target): new_w1, new_h1 = self.new_size w, h = image.size new_w = int(w * self.crop_fraction) new_h = int(new_w*new_h1/new_w1) i=0 for i in range(4): if new_h >= h: i += 0.05 new_w = int(w * (self.crop_fraction - i)) new_h = int(new_w*new_h1/new_w1) if new_h < h: continue if new_h >= h: return image, target boxes = target["boxes"] if 'keypoints' in target: keypoints = target["keypoints"] else: keypoints = [] for i in range(len(boxes)): keypoints.append(torch.zeros((2,3))) # Attempt to find a suitable crop region success = False for _ in range(100): # Max 100 attempts to find a valid crop top = random.randint(0, h - new_h) left = random.randint(0, w - new_w) crop_region = [left, top, left + new_w, top + new_h] # Check how many objects are fully contained in this region contained_boxes = [] contained_keypoints = [] for box, kp in zip(boxes, keypoints): if box[0] >= crop_region[0] and box[1] >= crop_region[1] and box[2] <= crop_region[2] and box[3] <= crop_region[3]: # Adjust box and keypoints coordinates new_box = box - torch.tensor([crop_region[0], crop_region[1], crop_region[0], crop_region[1]]) new_kp = kp - torch.tensor([crop_region[0], crop_region[1], 0]) contained_boxes.append(new_box) contained_keypoints.append(new_kp) if len(contained_boxes) >= self.min_objects: success = True break if success: # Perform the actual crop image = F.crop(image, top, left, new_h, new_w) target["boxes"] = torch.stack(contained_boxes) if contained_boxes else torch.zeros((0, 4)) if 'keypoints' in target: target["keypoints"] = torch.stack(contained_keypoints) if contained_keypoints else torch.zeros((0, 2, 4)) return image, target class RandomFlip: def __init__(self, h_flip_prob=0.5, v_flip_prob=0.5): """ Initializes the RandomFlip with probabilities for flipping. Parameters: - h_flip_prob (float): Probability of applying a horizontal flip to the image. - v_flip_prob (float): Probability of applying a vertical flip to the image. """ self.h_flip_prob = h_flip_prob self.v_flip_prob = v_flip_prob def __call__(self, image, target): """ Applies random horizontal and/or vertical flip to the image and updates target data accordingly. Parameters: - image (PIL Image): The image to be flipped. - target (dict): The target dictionary containing 'boxes' and 'keypoints'. Returns: - PIL Image, dict: The flipped image and its updated target dictionary. """ if random.random() < self.h_flip_prob: image = F.hflip(image) w, _ = image.size # Get the new width of the image after flip for bounding box adjustment # Adjust bounding boxes for horizontal flip for i, box in enumerate(target['boxes']): xmin, ymin, xmax, ymax = box target['boxes'][i] = torch.tensor([w - xmax, ymin, w - xmin, ymax], dtype=torch.float32) # Adjust keypoints for horizontal flip if 'keypoints' in target: new_keypoints = [] for keypoints_for_object in target['keypoints']: flipped_keypoints_for_object = [] for kp in keypoints_for_object: x, y = kp[:2] new_x = w - x flipped_keypoints_for_object.append(torch.tensor([new_x, y] + list(kp[2:]))) new_keypoints.append(torch.stack(flipped_keypoints_for_object)) target['keypoints'] = torch.stack(new_keypoints) if random.random() < self.v_flip_prob: image = F.vflip(image) _, h = image.size # Get the new height of the image after flip for bounding box adjustment # Adjust bounding boxes for vertical flip for i, box in enumerate(target['boxes']): xmin, ymin, xmax, ymax = box target['boxes'][i] = torch.tensor([xmin, h - ymax, xmax, h - ymin], dtype=torch.float32) # Adjust keypoints for vertical flip if 'keypoints' in target: new_keypoints = [] for keypoints_for_object in target['keypoints']: flipped_keypoints_for_object = [] for kp in keypoints_for_object: x, y = kp[:2] new_y = h - y flipped_keypoints_for_object.append(torch.tensor([x, new_y] + list(kp[2:]))) new_keypoints.append(torch.stack(flipped_keypoints_for_object)) target['keypoints'] = torch.stack(new_keypoints) return image, target class RandomRotate: def __init__(self, max_rotate_deg=20, rotate_proba=0.3): """ Initializes the RandomRotate with a maximum rotation angle and probability of rotating. Parameters: - max_rotate_deg (int): Maximum degree to rotate the image. - rotate_proba (float): Probability of applying rotation to the image. """ self.max_rotate_deg = max_rotate_deg self.rotate_proba = rotate_proba def __call__(self, image, target): """ Randomly rotates the image and updates the target data accordingly. Parameters: - image (PIL Image): The image to be rotated. - target (dict): The target dictionary containing 'boxes', 'labels', and 'keypoints'. Returns: - PIL Image, dict: The rotated image and its updated target dictionary. """ if random.random() < self.rotate_proba: angle = random.uniform(-self.max_rotate_deg, self.max_rotate_deg) image = F.rotate(image, angle, expand=False, fill=200) # Rotate bounding boxes w, h = image.size cx, cy = w / 2, h / 2 boxes = target["boxes"] new_boxes = [] for box in boxes: new_box = self.rotate_box(box, angle, cx, cy) new_boxes.append(new_box) target["boxes"] = torch.stack(new_boxes) # Rotate keypoints if 'keypoints' in target: new_keypoints = [] for keypoints in target["keypoints"]: new_kp = self.rotate_keypoints(keypoints, angle, cx, cy) new_keypoints.append(new_kp) target["keypoints"] = torch.stack(new_keypoints) return image, target def rotate_box(self, box, angle, cx, cy): """ Rotates a bounding box by a given angle around the center of the image. """ x1, y1, x2, y2 = box corners = torch.tensor([ [x1, y1], [x2, y1], [x2, y2], [x1, y2] ]) corners = torch.cat((corners, torch.ones(corners.shape[0], 1)), dim=1) M = cv2.getRotationMatrix2D((cx, cy), angle, 1) corners = torch.matmul(torch.tensor(M, dtype=torch.float32), corners.T).T x_ = corners[:, 0] y_ = corners[:, 1] x_min, x_max = torch.min(x_), torch.max(x_) y_min, y_max = torch.min(y_), torch.max(y_) return torch.tensor([x_min, y_min, x_max, y_max], dtype=torch.float32) def rotate_keypoints(self, keypoints, angle, cx, cy): """ Rotates keypoints by a given angle around the center of the image. """ new_keypoints = [] for kp in keypoints: x, y, v = kp point = torch.tensor([x, y, 1]) M = cv2.getRotationMatrix2D((cx, cy), angle, 1) new_point = torch.matmul(torch.tensor(M, dtype=torch.float32), point) new_keypoints.append(torch.tensor([new_point[0], new_point[1], v], dtype=torch.float32)) return torch.stack(new_keypoints) def rotate_90_box(box, angle, w, h): x1, y1, x2, y2 = box if angle == 90: return torch.tensor([y1,h-x2,y2,h-x1]) elif angle == 270 or angle == -90: return torch.tensor([w-y2,x1,w-y1,x2]) else: print("angle not supported") def rotate_90_keypoints(kp, angle, w, h): # Extract coordinates and visibility from each keypoint tensor x1, y1, v1 = kp[0][0], kp[0][1], kp[0][2] x2, y2, v2 = kp[1][0], kp[1][1], kp[1][2] # Swap x and y coordinates for each keypoint if angle == 90: new = [[y1, h-x1, v1], [y2, h-x2, v2]] elif angle == 270 or angle == -90: new = [[w-y1, x1, v1], [w-y2, x2, v2]] return torch.tensor(new, dtype=torch.float32) def rotate_vertical(image, target): # Rotate the image and target if the image is vertical new_boxes = [] angle = random.choice([-90,90]) image = F.rotate(image, angle, expand=True, fill=200) for box in target["boxes"]: new_box = rotate_90_box(box, angle, image.size[0], image.size[1]) new_boxes.append(new_box) target["boxes"] = torch.stack(new_boxes) if 'keypoints' in target: new_kp = [] for kp in target['keypoints']: new_key = rotate_90_keypoints(kp, angle, image.size[0], image.size[1]) new_kp.append(new_key) target['keypoints'] = torch.stack(new_kp) return image, target class BPMN_Dataset(Dataset): def __init__(self, annotations, transform=None, crop_transform=None, crop_prob=0.3, rotate_90_proba=0.2, flip_transform=None, rotate_transform=None, new_size=(1333,800),keep_ratio=False,resize=True, model_type='object', rotate_vertical=False): self.annotations = annotations print(f"Loaded {len(self.annotations)} annotations.") self.transform = transform self.crop_transform = crop_transform self.crop_prob = crop_prob self.flip_transform = flip_transform self.rotate_transform = rotate_transform self.resize = resize self.rotate_vertical = rotate_vertical self.new_size = new_size self.keep_ratio = keep_ratio self.model_type = model_type if model_type == 'object': self.dict = object_dict elif model_type == 'arrow': self.dict = arrow_dict self.rotate_90_proba = rotate_90_proba def __len__(self): return len(self.annotations) def __getitem__(self, idx): annotation = self.annotations[idx] image = annotation.img.convert("RGB") boxes = torch.tensor(np.array(annotation.boxes_ltrb), dtype=torch.float32) labels_names = [ann for ann in annotation.categories] #only keep the labels, boxes and keypoints that are in the class_dict kept_indices = [i for i, ann in enumerate(annotation.categories) if ann in self.dict.values()] boxes = boxes[kept_indices] labels_names = [ann for i, ann in enumerate(labels_names) if i in kept_indices] labels_id = torch.tensor([(list(self.dict.values()).index(ann)) for ann in labels_names], dtype=torch.int64) # Initialize keypoints tensor max_keypoints = 2 keypoints = torch.zeros((len(labels_id), max_keypoints, 3), dtype=torch.float32) ii=0 for i, ann in enumerate(annotation.annotations): #only keep the keypoints that are in the kept indices if i not in kept_indices: continue if ann.category in ["sequenceFlow", "messageFlow", "dataAssociation"]: # Fill the keypoints tensor for this annotation, mark as visible (1) kp = np.array(ann.keypoints, dtype=np.float32).reshape(-1, 3) kp = kp[:,:2] visible = np.ones((kp.shape[0], 1), dtype=np.float32) kp = np.hstack([kp, visible]) keypoints[ii, :kp.shape[0], :] = torch.tensor(kp, dtype=torch.float32) ii += 1 area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]) if self.model_type == 'object': target = { "boxes": boxes, "labels": labels_id, #"area": area, #"keypoints": keypoints, } elif self.model_type == 'arrow': target = { "boxes": boxes, "labels": labels_id, #"area": area, "keypoints": keypoints, } # Randomly apply flip transform if self.flip_transform: image, target = self.flip_transform(image, target) # Randomly apply rotate transform if self.rotate_transform: image, target = self.rotate_transform(image, target) # Randomly apply the custom cropping transform if self.crop_transform and random.random() < self.crop_prob: image, target = self.crop_transform(image, target) # Rotate vertical image if self.rotate_vertical and random.random() < self.rotate_90_proba: image, target = rotate_vertical(image, target) if self.resize: if self.keep_ratio: original_size = image.size # Calculate scale to fit the new size while maintaining aspect ratio scale = min(self.new_size[0] / original_size[0], self.new_size[1] / original_size[1]) new_scaled_size = (int(original_size[0] * scale), int(original_size[1] * scale)) target['boxes'] = resize_boxes(target['boxes'], (image.size[0],image.size[1]), (new_scaled_size)) if 'area' in target: target['area'] = (target['boxes'][:, 3] - target['boxes'][:, 1]) * (target['boxes'][:, 2] - target['boxes'][:, 0]) if 'keypoints' in target: for i in range(len(target['keypoints'])): target['keypoints'][i] = resize_keypoints(target['keypoints'][i], (image.size[0],image.size[1]), (new_scaled_size)) # Resize image to new scaled size image = F.resize(image, (new_scaled_size[1], new_scaled_size[0])) # Pad the resized image to make it exactly the desired size padding = [0, 0, self.new_size[0] - new_scaled_size[0], self.new_size[1] - new_scaled_size[1]] image = F.pad(image, padding, fill=200, padding_mode='constant') else: target['boxes'] = resize_boxes(target['boxes'], (image.size[0],image.size[1]), self.new_size) if 'area' in target: target['area'] = (target['boxes'][:, 3] - target['boxes'][:, 1]) * (target['boxes'][:, 2] - target['boxes'][:, 0]) if 'keypoints' in target: for i in range(len(target['keypoints'])): target['keypoints'][i] = resize_keypoints(target['keypoints'][i], (image.size[0],image.size[1]), self.new_size) image = F.resize(image, (self.new_size[1], self.new_size[0])) return self.transform(image), target def collate_fn(batch): """ Custom collation function for DataLoader that handles batches of images and targets. This function ensures that images are properly batched together using PyTorch's default collation, while keeping the targets (such as bounding boxes and labels) in a list of dictionaries, as each image might have a different number of objects detected. Parameters: - batch (list): A list of tuples, where each tuple contains an image and its corresponding target dictionary. Returns: - Tuple containing: - Tensor: Batched images. - List of dicts: Targets corresponding to each image in the batch. """ images, targets = zip(*batch) # Unzip the batch into separate lists for images and targets. # Batch images using the default collate function which handles tensors, numpy arrays, numbers, etc. images = default_collate(images) return images, targets def create_loader(new_size,transformation, annotations1, annotations2=None, batch_size=4, crop_prob=0.2, crop_fraction=0.7, min_objects=3, h_flip_prob=0.3, v_flip_prob=0.3, max_rotate_deg=20, rotate_90_proba=0.2, rotate_proba=0.3, seed=42, resize=True, rotate_vertical=False, keep_ratio=False, model_type = 'object'): """ Creates a DataLoader for BPMN datasets with optional transformations and concatenation of two datasets. Parameters: - transformation (callable): Transformation function to apply to each image (e.g., normalization). - annotations1 (list): Primary list of annotations. - annotations2 (list, optional): Secondary list of annotations to concatenate with the first. - batch_size (int): Number of images per batch. - crop_prob (float): Probability of applying the crop transformation. - crop_fraction (float): Fraction of the original width to use when cropping. - min_objects (int): Minimum number of objects required to be within the crop. - h_flip_prob (float): Probability of applying horizontal flip. - v_flip_prob (float): Probability of applying vertical flip. - seed (int): Seed for random number generators for reproducibility. - resize (bool): Flag indicating whether to resize images after transformations. Returns: - DataLoader: Configured data loader for the dataset. """ # Initialize custom transformations for cropping and flipping custom_crop_transform = RandomCrop(new_size,crop_fraction, min_objects) custom_flip_transform = RandomFlip(h_flip_prob, v_flip_prob) custom_rotate_transform = RandomRotate(max_rotate_deg, rotate_proba) # Create the primary dataset dataset = BPMN_Dataset( annotations=annotations1, transform=transformation, crop_transform=custom_crop_transform, crop_prob=crop_prob, rotate_90_proba=rotate_90_proba, flip_transform=custom_flip_transform, rotate_transform=custom_rotate_transform, rotate_vertical=rotate_vertical, new_size=new_size, keep_ratio=keep_ratio, model_type=model_type, resize=resize ) # Optionally concatenate a second dataset if annotations2: dataset2 = BPMN_Dataset( annotations=annotations2, transform=transformation, crop_transform=custom_crop_transform, crop_prob=crop_prob, rotate_90_proba=rotate_90_proba, flip_transform=custom_flip_transform, rotate_vertical=rotate_vertical, new_size=new_size, keep_ratio=keep_ratio, model_type=model_type, resize=resize ) dataset = ConcatDataset([dataset, dataset2]) # Concatenate the two datasets # Set the seed for reproducibility in random operations within transformations and data loading random.seed(seed) torch.manual_seed(seed) # Create the DataLoader with the dataset data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn) return data_loader def write_results(name_model,metrics_list,start_epoch): with open('./results/'+ name_model+ '.txt', 'w') as f: for i in range(len(metrics_list[0])): f.write(f"{i+1+start_epoch},{metrics_list[0][i]},{metrics_list[1][i]},{metrics_list[2][i]},{metrics_list[3][i]},{metrics_list[4][i]},{metrics_list[5][i]},{metrics_list[6][i]},{metrics_list[7][i]},{metrics_list[8][i]},{metrics_list[9][i]} \n") def find_other_keypoint(idx, keypoints, boxes): box = boxes[idx] key1,key2 = keypoints[idx] x1, y1, x2, y2 = box center = ((x1 + x2) // 2, (y1 + y2) // 2) average_keypoint = (key1 + key2) // 2 #find the opposite keypoint to the center if average_keypoint[0] < center[0]: x = center[0] + abs(center[0] - average_keypoint[0]) else: x = center[0] - abs(center[0] - average_keypoint[0]) if average_keypoint[1] < center[1]: y = center[1] + abs(center[1] - average_keypoint[1]) else: y = center[1] - abs(center[1] - average_keypoint[1]) return x, y, average_keypoint[0], average_keypoint[1] def filter_overlap_boxes(boxes, scores, labels, keypoints, iou_threshold=0.5): """ Filters overlapping boxes based on the Intersection over Union (IoU) metric, keeping only the boxes with the highest scores. Parameters: - boxes (np.ndarray): Array of bounding boxes with shape (N, 4), where each row contains [x_min, y_min, x_max, y_max]. - scores (np.ndarray): Array of scores for each box, reflecting the confidence of detection. - labels (np.ndarray): Array of labels corresponding to each box. - keypoints (np.ndarray): Array of keypoints associated with each box. - iou_threshold (float): Threshold for IoU above which a box is considered overlapping. Returns: - tuple: Filtered boxes, scores, labels, and keypoints. """ # Calculate the area of each bounding box to use in IoU calculation. areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # Sort the indices of the boxes based on their scores in descending order. order = scores.argsort()[::-1] keep = [] # List to store indices of boxes to keep. while order.size > 0: # Take the first index (highest score) from the sorted list. i = order[0] keep.append(i) # Add this index to 'keep' list. # Compute the coordinates of the intersection rectangle. xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0]) yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1]) xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2]) yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3]) # Compute the area of the intersection rectangle. w = np.maximum(0.0, xx2 - xx1) h = np.maximum(0.0, yy2 - yy1) inter = w * h # Calculate IoU and find boxes with IoU less than the threshold to keep. iou = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(iou <= iou_threshold)[0] # Update the list of box indices to consider in the next iteration. order = order[inds + 1] # Skip the first element since it's already included in 'keep'. # Use the indices in 'keep' to select the boxes, scores, labels, and keypoints to return. boxes = boxes[keep] scores = scores[keep] labels = labels[keep] keypoints = keypoints[keep] return boxes, scores, labels, keypoints def draw_annotations(image, target=None, prediction=None, full_prediction=None, text_predictions=None, model_dict=class_dict, draw_keypoints=False, draw_boxes=False, draw_text=False, draw_links=False, draw_twins=False, write_class=False, write_score=False, write_text=False, write_idx=False, score_threshold=0.4, keypoints_correction=False, only_print=None, axis=False, return_image=False, new_size=(1333,800), resize=False): """ Draws annotations on images including bounding boxes, keypoints, links, and text. Parameters: - image (np.array): The image on which annotations will be drawn. - target (dict): Ground truth data containing boxes, labels, etc. - prediction (dict): Prediction data from a model. - full_prediction (dict): Additional detailed prediction data, potentially including relationships. - text_predictions (tuple): OCR text predictions containing bounding boxes and texts. - model_dict (dict): Mapping from class IDs to class names. - draw_keypoints (bool): Flag to draw keypoints. - draw_boxes (bool): Flag to draw bounding boxes. - draw_text (bool): Flag to draw text annotations. - draw_links (bool): Flag to draw links between annotations. - draw_twins (bool): Flag to draw twins keypoints. - write_class (bool): Flag to write class names near the annotations. - write_score (bool): Flag to write scores near the annotations. - write_text (bool): Flag to write OCR recognized text. - score_threshold (float): Threshold for scores above which annotations will be drawn. - only_print (str): Specific class name to filter annotations by. - resize (bool): Whether to resize annotations to fit the image size. """ # Convert image to RGB (if not already in that format) if prediction is None: image = image.squeeze(0).permute(1, 2, 0).cpu().numpy() image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image_copy = image.copy() scale = max(image.shape[0], image.shape[1]) / 1000 # Function to draw bounding boxes and keypoints def draw(data,is_prediction=False): """ Helper function to draw annotations based on provided data. """ for i in range(len(data['boxes'])): if is_prediction: box = data['boxes'][i].tolist() x1, y1, x2, y2 = box if resize: x1, y1, x2, y2 = resize_boxes(np.array([box]), new_size, (image_copy.shape[1],image_copy.shape[0]))[0] score = data['scores'][i].item() if score < score_threshold: continue else: box = data['boxes'][i].tolist() x1, y1, x2, y2 = box if draw_boxes: if only_print is not None: if data['labels'][i] != list(model_dict.values()).index(only_print): continue cv2.rectangle(image_copy, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 0) if is_prediction else (0, 0, 0), int(2*scale)) if is_prediction and write_score: cv2.putText(image_copy, str(round(score, 2)), (int(x1), int(y1) + int(15*scale)), cv2.FONT_HERSHEY_SIMPLEX, scale/2, (100,100, 255), 2) if write_class and 'labels' in data: class_id = data['labels'][i].item() cv2.putText(image_copy, model_dict[class_id], (int(x1), int(y1) - int(2*scale)), cv2.FONT_HERSHEY_SIMPLEX, scale/2, (255, 100, 100), 2) if write_idx: cv2.putText(image_copy, str(i), (int(x1) + int(15*scale), int(y1) + int(15*scale)), cv2.FONT_HERSHEY_SIMPLEX, 2*scale, (0,0, 0), 2) # Draw keypoints if available if draw_keypoints and 'keypoints' in data: if is_prediction and keypoints_correction: for idx, (key1, key2) in enumerate(data['keypoints']): if data['labels'][idx] not in [list(model_dict.values()).index('sequenceFlow'), list(model_dict.values()).index('messageFlow'), list(model_dict.values()).index('dataAssociation')]: continue # Calculate the Euclidean distance between the two keypoints distance = np.linalg.norm(key1[:2] - key2[:2]) if distance < 5: x_new,y_new, x,y = find_other_keypoint(idx, data['keypoints'], data['boxes']) data['keypoints'][idx][0] = torch.tensor([x_new, y_new,1]) data['keypoints'][idx][1] = torch.tensor([x, y,1]) print("keypoint has been changed") for i in range(len(data['keypoints'])): kp = data['keypoints'][i] for j in range(kp.shape[0]): if is_prediction and data['labels'][i] != list(model_dict.values()).index('sequenceFlow') and data['labels'][i] != list(model_dict.values()).index('messageFlow') and data['labels'][i] != list(model_dict.values()).index('dataAssociation'): continue if is_prediction: score = data['scores'][i] if score < score_threshold: continue x,y,v = np.array(kp[j]) if resize: x, y, v = resize_keypoints(np.array([kp[j]]), new_size, (image_copy.shape[1],image_copy.shape[0]))[0] if j == 0: cv2.circle(image_copy, (int(x), int(y)), int(5*scale), (0, 0, 255), -1) else: cv2.circle(image_copy, (int(x), int(y)), int(5*scale), (255, 0, 0), -1) # Draw text predictions if available if (draw_text or write_text) and text_predictions is not None: for i in range(len(text_predictions[0])): x1, y1, x2, y2 = text_predictions[0][i] text = text_predictions[1][i] if resize: x1, y1, x2, y2 = resize_boxes(np.array([[float(x1), float(y1), float(x2), float(y2)]]), new_size, (image_copy.shape[1],image_copy.shape[0]))[0] if draw_text: cv2.rectangle(image_copy, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), int(2*scale)) if write_text: cv2.putText(image_copy, text, (int(x1 + int(2*scale)), int((y1+y2)/2) ), cv2.FONT_HERSHEY_SIMPLEX, scale/2, (0,0, 0), 2) def draw_with_links(full_prediction): '''Draws links between objects based on the full prediction data.''' #check if keypoints detected are the same if draw_twins and full_prediction is not None: # Pre-calculate indices for performance circle_color = (0, 255, 0) # Green color for the circle circle_radius = int(10 * scale) # Circle radius scaled by image scale for idx, (key1, key2) in enumerate(full_prediction['keypoints']): if full_prediction['labels'][idx] not in [list(model_dict.values()).index('sequenceFlow'), list(model_dict.values()).index('messageFlow'), list(model_dict.values()).index('dataAssociation')]: continue # Calculate the Euclidean distance between the two keypoints distance = np.linalg.norm(key1[:2] - key2[:2]) if distance < 10: x_new,y_new, x,y = find_other_keypoint(idx,full_prediction) cv2.circle(image_copy, (int(x), int(y)), circle_radius, circle_color, -1) cv2.circle(image_copy, (int(x_new), int(y_new)), circle_radius, (0,0,0), -1) # Draw links between objects if draw_links==True and full_prediction is not None: for i, (start_idx, end_idx) in enumerate(full_prediction['links']): if start_idx is None or end_idx is None: continue start_box = full_prediction['boxes'][start_idx] end_box = full_prediction['boxes'][end_idx] current_box = full_prediction['boxes'][i] # Calculate the center of each bounding box start_center = ((start_box[0] + start_box[2]) // 2, (start_box[1] + start_box[3]) // 2) end_center = ((end_box[0] + end_box[2]) // 2, (end_box[1] + end_box[3]) // 2) current_center = ((current_box[0] + current_box[2]) // 2, (current_box[1] + current_box[3]) // 2) # Draw a line between the centers of the connected objects cv2.line(image_copy, (int(start_center[0]), int(start_center[1])), (int(current_center[0]), int(current_center[1])), (0, 0, 255), int(2*scale)) cv2.line(image_copy, (int(current_center[0]), int(current_center[1])), (int(end_center[0]), int(end_center[1])), (255, 0, 0), int(2*scale)) i+=1 # Draw GT annotations if target is not None: draw(target, is_prediction=False) # Draw predictions if prediction is not None: #prediction = prediction[0] draw(prediction, is_prediction=True) # Draw links with full predictions if full_prediction is not None: draw_with_links(full_prediction) # Display the image image_copy = cv2.cvtColor(image_copy, cv2.COLOR_BGR2RGB) plt.figure(figsize=(12, 12)) plt.imshow(image_copy) if axis==False: plt.axis('off') plt.show() if return_image: return image_copy def find_closest_object(keypoint, boxes, labels): """ Find the closest object to a keypoint based on their proximity. Parameters: - keypoint (numpy.ndarray): The coordinates of the keypoint. - boxes (numpy.ndarray): The bounding boxes of the objects. Returns: - int or None: The index of the closest object to the keypoint, or None if no object is found. """ min_distance = float('inf') closest_object_idx = None # Iterate over each bounding box for i, box in enumerate(boxes): if labels[i] in [list(class_dict.values()).index('sequenceFlow'), list(class_dict.values()).index('messageFlow'), list(class_dict.values()).index('dataAssociation'), #list(class_dict.values()).index('pool'), list(class_dict.values()).index('lane')]: continue x1, y1, x2, y2 = box top = ((x1+x2)/2, y1) bottom = ((x1+x2)/2, y2) left = (x1, (y1+y2)/2) right = (x2, (y1+y2)/2) points = [left, top , right, bottom] pos_dict = {0:'left', 1:'top', 2:'right', 3:'bottom'} # Calculate the distance between the keypoint and the center of the bounding box for pos, (point) in enumerate(points): distance = np.linalg.norm(keypoint[:2] - point) # Update the closest object index if this object is closer if distance < min_distance: min_distance = distance closest_object_idx = i best_point = pos_dict[pos] return closest_object_idx, best_point