import cv2 import numpy as np import random from config_files.yolo_config import CLASS_NAME, CLASS_NUM class Inference: def __init__(self, onnx_model_path, model_input_shape, classes_txt_file, run_with_cuda): self.model_path = onnx_model_path self.model_shape = model_input_shape self.classes_path = classes_txt_file self.cuda_enabled = run_with_cuda self.letter_box_for_square = True self.model_score_threshold = 0.3 self.model_nms_threshold = 0.5 self.classes = [] self.load_onnx_network() self.load_classes_from_file() def sigmoid(self, x): return 1 / (1 + np.exp(-x)) def run_inference(self, input_image): model_input = input_image if self.letter_box_for_square and self.model_shape[0] == self.model_shape[1]: model_input = self.format_to_square(model_input) blob = cv2.dnn.blobFromImage(model_input, 1.0 / 255.0, self.model_shape, (0, 0, 0), True, False) self.net.setInput(blob) outputs = self.net.forward(self.net.getUnconnectedOutLayersNames()) outputs_bbox = outputs[0] outputs_mask = outputs[1] detections = self.process_detections(outputs_bbox, model_input) mask_maps = self.process_mask_output(detections, outputs_mask, model_input.shape) return detections, mask_maps def process_detections(self, outputs_bbox, model_input): # Assuming outputs_bbox is already in the (x, y, w, h, confidence, class_probs...) format x_factor = model_input.shape[1] / self.model_shape[0] y_factor = model_input.shape[0] / self.model_shape[1] class_ids = [] confidences = [] mask_coefficients = [] boxes = [] for detection in outputs_bbox[0].T: # This segmentation model uses yolact architecture to predict mask # the output tensor dimension for yolo-v8-seg is B x [X, Y, W, H, C1, C2, ..., P1, ...,P32] * 8400 # where C{n} are confidence score for each class # and P{n} are coefficient for each proto masks. (32 by default) scores_classification = detection[4:4+CLASS_NUM] scores_segmentation = detection[4+CLASS_NUM:] class_id = np.argmax(scores_classification, axis=0) confidence = scores_classification[class_id] thres = self.model_score_threshold w_thres = 20 h_thres = 20 x, y, w, h = detection[:4] # if bboxes are too small, it just skips, and it is not a bad idea since we do not need to detect small areas if w < w_thres or h < h_thres: continue if confidence > thres: left = int((x - 0.5 * w) * x_factor) top = int((y - 0.5 * h) * y_factor) width = int(w * x_factor) height = int(h * y_factor) boxes.append([left, top, width, height]) confidences.append(float(confidence)) mask_coefficients.append(scores_segmentation) class_ids.append(class_id) confidences = (confidences) indices = cv2.dnn.NMSBoxes(boxes, confidences, self.model_score_threshold, self.model_nms_threshold) detections = [] for i in indices: idx = i result = { 'class_id': class_ids[i], 'confidence': confidences[i], 'mask_coefficients': np.array(mask_coefficients[i]), 'box': boxes[idx], 'class_name': self.classes[class_ids[i]], 'color': (random.randint(100, 255), random.randint(100, 255), random.randint(100, 255)) } detections.append(result) return detections def process_mask_output(self, detections, proto_masks, image_shape): if not detections: return [] batch_size, num_protos, proto_height, proto_width = proto_masks.shape # Correct shape unpacking full_masks = np.zeros((len(detections), image_shape[0], image_shape[1]), dtype=np.float32) for idx, det in enumerate(detections): box = det['box'] x1, y1, w, h = box # print(f"x1 : {x1}, y1 : {y1}, w: {w}, h: {h}") x1, y1, x2, y2 = x1, y1, x1 + w, y1 + h #... why the model outputs ... negative values?... if x1 <= 0 : w += x1 x1 = 0 if y1 <= 0 : h += y1 y1 = 0 # To handle edge cases where you get bboxes that pass beyond the original size of image_binary if y2 > image_shape[1]: h = image_shape[1] - y1 if x2 > image_shape[0]: w = image_shape[1] - y1 # print(f"x2: {x2}, y2 : {y2}") # Get the corresponding mask coefficients for this detection coeffs = det["mask_coefficients"] # Compute the linear combination of proto masks # for now, plural batch operation is not supported, and this is the point where you should start. # instead of hardcoded proto_masks[0], do some iterative operation. mask = np.tensordot(coeffs, proto_masks[0], axes=[0, 0]) # Dot product along the number of prototypes # Resize mask to the bounding box size, using sigmoid to normalize resized_mask = cv2.resize(mask, (w, h)) resized_mask = self.sigmoid(resized_mask) # Threshold to create a binary mask final_mask = (resized_mask > 0.5).astype(np.uint8) # Place the mask in the corresponding location on a full-sized mask image_binary full_mask = np.zeros((image_shape[0], image_shape[1]), dtype=np.uint8) # print("---------") # print(f"x1 : {x1}, y1 : {y1}, w: {w}, h: {h}") # print(f"x2: {x2}, y2 : {y2}") # print(final_mask.shape) # print(full_mask[y1:y2, x1:x2].shape) full_mask[y1:y2, x1:x2] = final_mask # Combine the mask with the masks of other detections full_masks[idx] = full_mask all_mask = full_masks.sum(axis=0) # Append a dimension so that cv2 can understand this as an image. all_mask = all_mask.reshape((image_shape[0], image_shape[1], 1)) return all_mask.astype(np.uint8) def load_classes_from_file(self): with open(self.classes_path, 'r') as f: self.classes = f.read().strip().split('\n') def load_onnx_network(self): self.net = cv2.dnn.readNetFromONNX(self.model_path) if self.cuda_enabled: print("\nRunning on CUDA") self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA) else: print("\nRunning on CPU") self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU) def format_to_square(self, source): col, row = source.shape[1], source.shape[0] max_side = max(col, row) result = np.zeros((max_side, max_side, 3), dtype=np.uint8) result[0:row, 0:col] = source return result def overlay_mask(image, mask, color=(0, 255, 0), alpha=0.5): """ Overlays a mask onto an image_binary using a specified color and transparency level. Parameters: image (np.ndarray): The original image_binary. mask (np.ndarray): The mask to overlay. Must be the same size as the image_binary. color (tuple): The color for the mask overlay in BGR format (default is green). alpha (float): Transparency factor for the mask; 0 is fully transparent, 1 is opaque. Returns: np.ndarray: The image_binary with the overlay. """ # Ensure the mask is a binary mask mask = (mask > 0).astype(np.uint8) # Convert mask to binary if not already # Create an overlay with the same size as the image_binary but only using the mask area overlay = np.zeros_like(image, dtype=np.uint8) overlay[mask == 1] = color # Blend the overlay with the image_binary using the alpha factor return cv2.addWeighted(src1=overlay, alpha=alpha, src2=image, beta=1 - alpha, gamma=0) def test(): import time # Path to your ONNX model and classes text file model_path = 'yoloseg/weight/best.onnx' classes_txt_file = 'config_files/yolo_config.txt' image_path = 'yoloseg/img3.jpg' model_input_shape = (640, 640) inference_engine = Inference( onnx_model_path=model_path, model_input_shape=model_input_shape, classes_txt_file=classes_txt_file, run_with_cuda=True ) # Load an image_binary img = cv2.imread(image_path) if img is None: print("Error loading image_binary") return img = cv2.resize(img, model_input_shape) # Run inference t1 = time.time() detections, mask_maps = inference_engine.run_inference(img) t2 = time.time() print(t2-t1) # Display results for detection in detections: x, y, w, h = detection['box'] class_name = detection['class_name'] confidence = detection['confidence'] cv2.rectangle(img, (x, y), (x+w, y+h), detection['color'], 2) label = f"{class_name}: {confidence:.2f}" cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, detection['color'], 2) # Show the image_binary # cv2.imshow('Detections', img) # cv2.waitKey(0) # cv2.destroyAllWindows() # If you also want to display segmentation maps, you would need additional handling here # Example for displaying first mask if available: if mask_maps is not None: seg_image = overlay_mask(img, mask_maps[0], color=(0, 255, 0), alpha=0.3) cv2.imshow("segmentation", seg_image) cv2.waitKey(0) cv2.destroyAllWindows() # def test2(): # import time # import glob # # # Path to your ONNX model and classes text file # model_path = 'yoloseg/weight/best.onnx' # classes_txt_file = 'config_files/yolo_config.txt' # # model_input_shape = (640, 640) # inference_engine = Inference( # onnx_model_path=model_path, # model_input_shape=model_input_shape, # classes_txt_file=classes_txt_file, # run_with_cuda=True # ) # # image_dir = glob.glob("/home/juni/사진/sample_data/ex1/*.png") # # for iteration, image_path in enumerate(image_dir): # img = cv2.imread(image_path) # if img is None: # print("Error loading image_binary") # return # img = cv2.resize(img, model_input_shape) # # Run inference # t1 = time.time() # detections, mask_maps = inference_engine.run_inference(img) # t2 = time.time() # # print(t2-t1) # # # Display results # # for detection in detections: # # x, y, w, h = detection['box'] # # class_name = detection['class_name'] # # confidence = detection['confidence'] # # cv2.rectangle(img, (x, y), (x+w, y+h), detection['color'], 2) # # label = f"{class_name}: {confidence:.2f}" # # cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, detection['color'], 2) # # # if len(mask_maps) > 0 : # seg_image = overlay_mask(img, mask_maps[0], color=(0, 255, 0), alpha=0.3) # cv2.imwrite(f"result/{iteration}.png", seg_image) if __name__ == "__main__": pass # test2()