From 1d10dec7912654663d338ea4935275913f888453 Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Thu, 18 May 2023 15:38:29 +0700 Subject: [PATCH 1/7] Update train.py --- ultralytics/yolo/v8/segment/train.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py index acfe8b1..50e0815 100644 --- a/ultralytics/yolo/v8/segment/train.py +++ b/ultralytics/yolo/v8/segment/train.py @@ -14,7 +14,8 @@ from ultralytics.yolo.utils.tal import make_anchors from ultralytics.yolo.utils.torch_utils import de_parallel -from ..detect.train import Loss +from ultralytics.yolo.v8.detect.train import Loss + # BaseTrainer python usage From 02548005a5dd92f66465aad8498ee3e550aba58b Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Mon, 22 May 2023 10:28:28 +0700 Subject: [PATCH 2/7] Create predict_line_cam1.py --- .../yolo/v8/segment/predict_line_cam1.py | 242 ++++++++++++++++++ 1 file changed, 242 insertions(+) create mode 100644 ultralytics/yolo/v8/segment/predict_line_cam1.py diff --git a/ultralytics/yolo/v8/segment/predict_line_cam1.py b/ultralytics/yolo/v8/segment/predict_line_cam1.py new file mode 100644 index 0000000..caa0282 --- /dev/null +++ b/ultralytics/yolo/v8/segment/predict_line_cam1.py @@ -0,0 +1,242 @@ +# Ultralytics YOLO 🚀, GPL-3.0 license + +import hydra +import torch + +from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops +from ultralytics.yolo.utils.checks import check_imgsz +from ultralytics.yolo.utils.plotting import colors, save_one_box + +from ultralytics.yolo.v8.detect.predict import DetectionPredictor +from numpy import random + + +import cv2 +from deep_sort_pytorch.utils.parser import get_config +from deep_sort_pytorch.deep_sort import DeepSort +#Deque is basically a double ended queue in python, we prefer deque over list when we need to perform insertion or pop up operations +#at the same time +from collections import deque +import numpy as np +palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) +data_deque = {} + +deepsort = None + +def init_tracker(): + global deepsort + cfg_deep = get_config() + cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") + + deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT, + max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, + nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, + max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, + use_cuda=True) +########################################################################################## +def xyxy_to_xywh(*xyxy): + """" Calculates the relative bounding box from absolute pixel values. """ + bbox_left = min([xyxy[0].item(), xyxy[2].item()]) + bbox_top = min([xyxy[1].item(), xyxy[3].item()]) + bbox_w = abs(xyxy[0].item() - xyxy[2].item()) + bbox_h = abs(xyxy[1].item() - xyxy[3].item()) + x_c = (bbox_left + bbox_w / 2) + y_c = (bbox_top + bbox_h / 2) + w = bbox_w + h = bbox_h + return x_c, y_c, w, h + +def xyxy_to_tlwh(bbox_xyxy): + tlwh_bboxs = [] + for i, box in enumerate(bbox_xyxy): + x1, y1, x2, y2 = [int(i) for i in box] + top = x1 + left = y1 + w = int(x2 - x1) + h = int(y2 - y1) + tlwh_obj = [top, left, w, h] + tlwh_bboxs.append(tlwh_obj) + return tlwh_bboxs + +def compute_color_for_labels(label): + """ + Simple function that adds fixed color depending on the class + """ + if label == 0: #person + color = (85,45,255) + elif label == 2: # Car + color = (222,82,175) + elif label == 3: # Motobike + color = (0, 204, 255) + elif label == 5: # Bus + color = (0, 149, 255) + else: + color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] + return tuple(color) + + + +def UI_box(x, img, color=None, label=None, line_thickness=None): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + + img = cv2.rectangle(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color,-1, cv2.LINE_AA) + + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + + +def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)): + #cv2.line(img, line[0], line[1], (46,162,112), 3) + + height, width, _ = img.shape + # remove tracked point from buffer if object is lost + for key in list(data_deque): + if key not in identities: + data_deque.pop(key) + + for i, box in enumerate(bbox): + x1, y1, x2, y2 = [int(i) for i in box] + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + + # code to find center of bottom edge + center = (int((x2+x1)/ 2), int((y2+y2)/2)) + + # get ID of object + id = int(identities[i]) if identities is not None else 0 + + # create new buffer for new object + if id not in data_deque: + data_deque[id] = deque(maxlen= 64) + color = compute_color_for_labels(object_id[i]) + obj_name = names[object_id[i]] + label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name) + + # add center to buffer + data_deque[id].appendleft(center) + UI_box(box, img, label=label, color=color, line_thickness=2) + # draw trail + for i in range(1, len(data_deque[id])): + # check if on buffer value is none + if data_deque[id][i - 1] is None or data_deque[id][i] is None: + continue + # generate dynamic thickness of trails + thickness = int(np.sqrt(64 / float(i + i)) * 1.5) + # draw trails + cv2.line(img, data_deque[id][i - 1], data_deque[id][i], color, thickness) + return img + + +class SegmentationPredictor(DetectionPredictor): + + def postprocess(self, preds, img, orig_img): + masks = [] + # TODO: filter by classes + p = ops.non_max_suppression(preds[0], + self.args.conf, + self.args.iou, + agnostic=self.args.agnostic_nms, + max_det=self.args.max_det, + nm=32) + proto = preds[1][-1] + for i, pred in enumerate(p): + shape = orig_img[i].shape if self.webcam else orig_img.shape + if not len(pred): + continue + if self.args.retina_masks: + pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() + masks.append(ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2])) # HWC + else: + masks.append(ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)) # HWC + pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() + + return (p, masks) + + def write_results(self, idx, preds, batch): + p, im, im0 = batch + log_string = "" + if len(im.shape) == 3: + im = im[None] # expand for batch dim + self.seen += 1 + if self.webcam: # batch_size >= 1 + log_string += f'{idx}: ' + frame = self.dataset.count + else: + frame = getattr(self.dataset, 'frame', 0) + + self.data_path = p + self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') + log_string += '%gx%g ' % im.shape[2:] # print string + self.annotator = self.get_annotator(im0) + + preds, masks = preds + det = preds[idx] + if len(det) == 0: + return log_string + # Segments + mask = masks[idx] + if self.args.save_txt: + segments = [ + ops.scale_segments(im0.shape if self.args.retina_masks else im.shape[2:], x, im0.shape, normalize=True) + for x in reversed(ops.masks2segments(mask))] + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Mask plotting + self.annotator.masks( + mask, + colors=[colors(x, True) for x in det[:, 5]], + im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() / + 255 if self.args.retina_masks else im[idx]) + + det = reversed(det[:, :6]) + self.all_outputs.append([det, mask]) + xywh_bboxs = [] + confs = [] + oids = [] + outputs = [] + # Write results + for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy) + xywh_obj = [x_c, y_c, bbox_w, bbox_h] + xywh_bboxs.append(xywh_obj) + confs.append([conf.item()]) + oids.append(int(cls)) + xywhs = torch.Tensor(xywh_bboxs) + confss = torch.Tensor(confs) + + outputs = deepsort.update(xywhs, confss, oids, im0) + if len(outputs) > 0: + bbox_xyxy = outputs[:, :4] + identities = outputs[:, -2] + object_id = outputs[:, -1] + + draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities) + return log_string + + +@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) +def predict(cfg): + init_tracker() + cfg.model = cfg.model or "yolov8n-seg.pt" + cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size + cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" + + predictor = SegmentationPredictor(cfg) + predictor() + + +if __name__ == "__main__": + predict() From 6796c03eef964f7960b899153fc0d31595955562 Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Mon, 22 May 2023 12:18:13 +0700 Subject: [PATCH 3/7] Update predict_line_cam1.py --- .../yolo/v8/segment/predict_line_cam1.py | 63 ++++++++++++++++--- 1 file changed, 55 insertions(+), 8 deletions(-) diff --git a/ultralytics/yolo/v8/segment/predict_line_cam1.py b/ultralytics/yolo/v8/segment/predict_line_cam1.py index caa0282..f4a0962 100644 --- a/ultralytics/yolo/v8/segment/predict_line_cam1.py +++ b/ultralytics/yolo/v8/segment/predict_line_cam1.py @@ -9,7 +9,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor from numpy import random - +import math import cv2 from deep_sort_pytorch.utils.parser import get_config @@ -20,9 +20,17 @@ import numpy as np palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) data_deque = {} - +counters = [] deepsort = None - +rectangle_top_left_back = (750, 470) +rectangle_bottom_right_back = (1279, 719) +rectangle_top_left = (141, 151) +rectangle_bottom_right = (1150, 460) +font_scale = 1 +font_thickness = 2 +text_position = (10, 50) +font = cv2.FONT_HERSHEY_SIMPLEX +font_color = (255, 255, 255) def init_tracker(): global deepsort cfg_deep = get_config() @@ -46,6 +54,12 @@ def xyxy_to_xywh(*xyxy): h = bbox_h return x_c, y_c, w, h +def check_rect_overlap(R1, R2): + if (R1[0]>=R2[2]) or (R1[2]<=R2[0]) or (R1[3]<=R2[1]) or (R1[1]>=R2[3]): + return False + else: + return True + def xyxy_to_tlwh(bbox_xyxy): tlwh_bboxs = [] for i, box in enumerate(bbox_xyxy): @@ -94,7 +108,7 @@ def UI_box(x, img, color=None, label=None, line_thickness=None): def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)): #cv2.line(img, line[0], line[1], (46,162,112), 3) - + counter =0 height, width, _ = img.shape # remove tracked point from buffer if object is lost for key in list(data_deque): @@ -102,6 +116,7 @@ def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)): data_deque.pop(key) for i, box in enumerate(bbox): + x1, y1, x2, y2 = [int(i) for i in box] x1 += offset[0] x2 += offset[0] @@ -160,8 +175,14 @@ def postprocess(self, preds, img, orig_img): pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() return (p, masks) - def write_results(self, idx, preds, batch): + counter =0 + count = 0 + min_counter = 0 + max_counter =0 + avg_counter = 0 + total_count = 0 + frame_count =0 p, im, im0 = batch log_string = "" if len(im.shape) == 3: @@ -202,30 +223,56 @@ def write_results(self, idx, preds, batch): 255 if self.args.retina_masks else im[idx]) det = reversed(det[:, :6]) + counter +=len(det) + print(counter) self.all_outputs.append([det, mask]) xywh_bboxs = [] confs = [] oids = [] outputs = [] # Write results + counters.append(counter) + print(f"objects detected {count}") + print(f"Total objects detected: {counter}") + max_counter = max(counters) + min_counter = min(counters) + total_count = sum(counters) + frame_count += len(counters) + if frame_count > 0: + avg_count = total_count / frame_count + else: + avg_count = 0 + print(f"Max objects detected: {max_counter}") for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy) + bbow_xyxy = [tensor.item() for tensor in xyxy] + print("2") xywh_obj = [x_c, y_c, bbox_w, bbox_h] xywh_bboxs.append(xywh_obj) confs.append([conf.item()]) oids.append(int(cls)) + cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) + if check_rect_overlap(bbow_xyxy, rectangle_top_left_back+rectangle_bottom_right_back) or check_rect_overlap(bbow_xyxy, rectangle_top_left+rectangle_bottom_right) : + counter = counter -1 + if check_rect_overlap(bbow_xyxy, rectangle_top_left+rectangle_bottom_right): + count +=1 + else: + continue xywhs = torch.Tensor(xywh_bboxs) confss = torch.Tensor(confs) - outputs = deepsort.update(xywhs, confss, oids, im0) if len(outputs) > 0: bbox_xyxy = outputs[:, :4] identities = outputs[:, -2] object_id = outputs[:, -1] - + print("1") + cv2.putText(im0, f'piglets detected: {counter} Max: {max_counter} Avg: {math.ceil(avg_count)} Min: {min_counter}', text_position, font, font_scale, font_color, font_thickness) + cv2.rectangle(im0, rectangle_top_left, rectangle_bottom_right, (255, 255, 255), 2) + cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities) return log_string - + @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) def predict(cfg): From e9805034947e6465f6d0544395c336f65e069438 Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Mon, 22 May 2023 16:42:45 +0700 Subject: [PATCH 4/7] Update predict_line_cam1.py --- .../yolo/v8/segment/predict_line_cam1.py | 80 ++++++++++++------- 1 file changed, 52 insertions(+), 28 deletions(-) diff --git a/ultralytics/yolo/v8/segment/predict_line_cam1.py b/ultralytics/yolo/v8/segment/predict_line_cam1.py index f4a0962..80a11bd 100644 --- a/ultralytics/yolo/v8/segment/predict_line_cam1.py +++ b/ultralytics/yolo/v8/segment/predict_line_cam1.py @@ -18,19 +18,23 @@ #at the same time from collections import deque import numpy as np +from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) data_deque = {} -counters = [] deepsort = None rectangle_top_left_back = (750, 470) rectangle_bottom_right_back = (1279, 719) rectangle_top_left = (141, 151) -rectangle_bottom_right = (1150, 460) +rectangle_bottom_right = (1279, 450) font_scale = 1 font_thickness = 2 text_position = (10, 50) font = cv2.FONT_HERSHEY_SIMPLEX font_color = (255, 255, 255) +line_thickness =3 +hide_labels=False, # hide labels +hide_conf=False, + def init_tracker(): global deepsort cfg_deep = get_config() @@ -212,9 +216,15 @@ def write_results(self, idx, preds, batch): # Print results for c in det[:, 5].unique(): - n = (det[:, 5] == c).sum() # detections per class - log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " # add to string + n = (det[:, 5] == c).sum() + log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " + + # for c in det[:, 5].unique(): + # n = (det[:, 5] == c).sum() # detections per class + # s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + # print(self.model.names) + annotator = Annotator(im0, line_width=line_thickness, example=str(self.model.names)) # Mask plotting self.annotator.masks( mask, @@ -230,25 +240,16 @@ def write_results(self, idx, preds, batch): confs = [] oids = [] outputs = [] + counters = [] # Write results - counters.append(counter) - print(f"objects detected {count}") - print(f"Total objects detected: {counter}") - max_counter = max(counters) - min_counter = min(counters) - total_count = sum(counters) - frame_count += len(counters) - if frame_count > 0: - avg_count = total_count / frame_count - else: - avg_count = 0 - print(f"Max objects detected: {max_counter}") - for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + # seg = segments[j].reshape(-1) # (n,2) to (n*2) + line = (cls, conf) x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy) bbow_xyxy = [tensor.item() for tensor in xyxy] - print("2") xywh_obj = [x_c, y_c, bbox_w, bbox_h] + label = f' {self.model.names[int(c)]} {conf:.2f}' xywh_bboxs.append(xywh_obj) confs.append([conf.item()]) oids.append(int(cls)) @@ -259,18 +260,41 @@ def write_results(self, idx, preds, batch): count +=1 else: continue + annotator.box_label(xyxy, label, color=colors(c, True)) + print(label) + counters.append(counter) + max_counter = max(counters) + min_counter = min(counters) + total_count = sum(counters) + frame_count += len(counters) + if frame_count > 0: + avg_count = total_count / frame_count + else: + avg_count = 0 + + print(f"objects detected {count}") + print(f"Total objects detected: {counter}") + print(f"Max objects detected: {max_counter}") xywhs = torch.Tensor(xywh_bboxs) confss = torch.Tensor(confs) - outputs = deepsort.update(xywhs, confss, oids, im0) - if len(outputs) > 0: - bbox_xyxy = outputs[:, :4] - identities = outputs[:, -2] - object_id = outputs[:, -1] - print("1") - cv2.putText(im0, f'piglets detected: {counter} Max: {max_counter} Avg: {math.ceil(avg_count)} Min: {min_counter}', text_position, font, font_scale, font_color, font_thickness) - cv2.rectangle(im0, rectangle_top_left, rectangle_bottom_right, (255, 255, 255), 2) - cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) - draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities) + cv2.putText(im0, f'piglets detected: {counter} Max: {max_counter} Avg: {math.ceil(avg_count)} Min: {min_counter}', text_position, font, font_scale, font_color, font_thickness) + cv2.putText(im0, f'objects detected {count}', (141, 500), font, font_scale, font_color,font_thickness) + cv2.rectangle(im0, rectangle_top_left, rectangle_bottom_right, (255, 255, 255), 2) + cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) + # outputs = deepsort.update(xywhs, confss, oids, im0) + # if len(outputs) >= 0: + # if check_rect_overlap(bbow_xyxy, rectangle_top_left_back+rectangle_bottom_right_back) or check_rect_overlap(bbow_xyxy, rectangle_top_left+rectangle_bottom_right) : + # counter = counter -1 + # if check_rect_overlap(bbow_xyxy, rectangle_top_left+rectangle_bottom_right): + # count +=1 + # else: + # bbox_xyxy = outputs[:, :4] + # identities = outputs[:, -2] + # object_id = outputs[:, -1] + # cv2.putText(im0, f'piglets detected: {counter} Max: {max_counter} Avg: {math.ceil(avg_count)} Min: {min_counter}', text_position, font, font_scale, font_color, font_thickness) + # cv2.rectangle(im0, rectangle_top_left, rectangle_bottom_right, (255, 255, 255), 2) + # cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) + # draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities) return log_string From 0f4fa5287ec39aa00e691bde6f4a6bc0b5b828a3 Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Mon, 22 May 2023 16:49:50 +0700 Subject: [PATCH 5/7] Create predict_cam2.py --- ultralytics/yolo/v8/segment/predict_cam2.py | 311 ++++++++++++++++++++ 1 file changed, 311 insertions(+) create mode 100644 ultralytics/yolo/v8/segment/predict_cam2.py diff --git a/ultralytics/yolo/v8/segment/predict_cam2.py b/ultralytics/yolo/v8/segment/predict_cam2.py new file mode 100644 index 0000000..27437ca --- /dev/null +++ b/ultralytics/yolo/v8/segment/predict_cam2.py @@ -0,0 +1,311 @@ +# Ultralytics YOLO 🚀, GPL-3.0 license + +import hydra +import torch + +from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops +from ultralytics.yolo.utils.checks import check_imgsz +from ultralytics.yolo.utils.plotting import colors, save_one_box + +from ultralytics.yolo.v8.detect.predict import DetectionPredictor +from numpy import random +import math + +import cv2 +from deep_sort_pytorch.utils.parser import get_config +from deep_sort_pytorch.deep_sort import DeepSort +#Deque is basically a double ended queue in python, we prefer deque over list when we need to perform insertion or pop up operations +#at the same time +from collections import deque +import numpy as np +from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box +palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) +data_deque = {} +deepsort = None +rectangle_top_left_back = (0, 96) +rectangle_bottom_right_back = (285, 435) +# rectangle_top_left = (141, 151) +# rectangle_bottom_right = (1279, 450) +font_scale = 1 +font_thickness = 2 +text_position = (10, 50) +font = cv2.FONT_HERSHEY_SIMPLEX +font_color = (255, 255, 255) +line_thickness =3 +hide_labels=False, # hide labels +hide_conf=False, + +def init_tracker(): + global deepsort + cfg_deep = get_config() + cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") + + deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT, + max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, + nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, + max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, + use_cuda=True) +########################################################################################## +def xyxy_to_xywh(*xyxy): + """" Calculates the relative bounding box from absolute pixel values. """ + bbox_left = min([xyxy[0].item(), xyxy[2].item()]) + bbox_top = min([xyxy[1].item(), xyxy[3].item()]) + bbox_w = abs(xyxy[0].item() - xyxy[2].item()) + bbox_h = abs(xyxy[1].item() - xyxy[3].item()) + x_c = (bbox_left + bbox_w / 2) + y_c = (bbox_top + bbox_h / 2) + w = bbox_w + h = bbox_h + return x_c, y_c, w, h + +def check_rect_overlap(R1, R2): + if (R1[0]>=R2[2]) or (R1[2]<=R2[0]) or (R1[3]<=R2[1]) or (R1[1]>=R2[3]): + return False + else: + return True + +def xyxy_to_tlwh(bbox_xyxy): + tlwh_bboxs = [] + for i, box in enumerate(bbox_xyxy): + x1, y1, x2, y2 = [int(i) for i in box] + top = x1 + left = y1 + w = int(x2 - x1) + h = int(y2 - y1) + tlwh_obj = [top, left, w, h] + tlwh_bboxs.append(tlwh_obj) + return tlwh_bboxs + +def compute_color_for_labels(label): + """ + Simple function that adds fixed color depending on the class + """ + if label == 0: #person + color = (85,45,255) + elif label == 2: # Car + color = (222,82,175) + elif label == 3: # Motobike + color = (0, 204, 255) + elif label == 5: # Bus + color = (0, 149, 255) + else: + color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] + return tuple(color) + + + +def UI_box(x, img, color=None, label=None, line_thickness=None): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + + img = cv2.rectangle(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color,-1, cv2.LINE_AA) + + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + + +def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)): + #cv2.line(img, line[0], line[1], (46,162,112), 3) + counter =0 + height, width, _ = img.shape + # remove tracked point from buffer if object is lost + for key in list(data_deque): + if key not in identities: + data_deque.pop(key) + + for i, box in enumerate(bbox): + + x1, y1, x2, y2 = [int(i) for i in box] + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + + # code to find center of bottom edge + center = (int((x2+x1)/ 2), int((y2+y2)/2)) + + # get ID of object + id = int(identities[i]) if identities is not None else 0 + + # create new buffer for new object + if id not in data_deque: + data_deque[id] = deque(maxlen= 64) + color = compute_color_for_labels(object_id[i]) + obj_name = names[object_id[i]] + label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name) + + # add center to buffer + data_deque[id].appendleft(center) + UI_box(box, img, label=label, color=color, line_thickness=2) + # draw trail + for i in range(1, len(data_deque[id])): + # check if on buffer value is none + if data_deque[id][i - 1] is None or data_deque[id][i] is None: + continue + # generate dynamic thickness of trails + thickness = int(np.sqrt(64 / float(i + i)) * 1.5) + # draw trails + cv2.line(img, data_deque[id][i - 1], data_deque[id][i], color, thickness) + return img + + +class SegmentationPredictor(DetectionPredictor): + + def postprocess(self, preds, img, orig_img): + masks = [] + # TODO: filter by classes + p = ops.non_max_suppression(preds[0], + self.args.conf, + self.args.iou, + agnostic=self.args.agnostic_nms, + max_det=self.args.max_det, + nm=32) + proto = preds[1][-1] + for i, pred in enumerate(p): + shape = orig_img[i].shape if self.webcam else orig_img.shape + if not len(pred): + continue + if self.args.retina_masks: + pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() + masks.append(ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2])) # HWC + else: + masks.append(ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)) # HWC + pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() + + return (p, masks) + def write_results(self, idx, preds, batch): + counter =0 + count = 0 + min_counter = 0 + max_counter =0 + avg_counter = 0 + total_count = 0 + frame_count =0 + p, im, im0 = batch + log_string = "" + if len(im.shape) == 3: + im = im[None] # expand for batch dim + self.seen += 1 + if self.webcam: # batch_size >= 1 + log_string += f'{idx}: ' + frame = self.dataset.count + else: + frame = getattr(self.dataset, 'frame', 0) + + self.data_path = p + self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') + log_string += '%gx%g ' % im.shape[2:] # print string + self.annotator = self.get_annotator(im0) + + preds, masks = preds + det = preds[idx] + if len(det) == 0: + return log_string + # Segments + mask = masks[idx] + if self.args.save_txt: + segments = [ + ops.scale_segments(im0.shape if self.args.retina_masks else im.shape[2:], x, im0.shape, normalize=True) + for x in reversed(ops.masks2segments(mask))] + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() + log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " + + # for c in det[:, 5].unique(): + # n = (det[:, 5] == c).sum() # detections per class + # s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # print(self.model.names) + annotator = Annotator(im0, line_width=line_thickness, example=str(self.model.names)) + # Mask plotting + self.annotator.masks( + mask, + colors=[colors(x, True) for x in det[:, 5]], + im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() / + 255 if self.args.retina_masks else im[idx]) + + det = reversed(det[:, :6]) + counter +=len(det) + print(counter) + self.all_outputs.append([det, mask]) + xywh_bboxs = [] + confs = [] + oids = [] + outputs = [] + counters = [] + # Write results + + for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + # seg = segments[j].reshape(-1) # (n,2) to (n*2) + line = (cls, conf) + x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy) + bbow_xyxy = [tensor.item() for tensor in xyxy] + xywh_obj = [x_c, y_c, bbox_w, bbox_h] + label = f' {self.model.names[int(c)]} {conf:.2f}' + xywh_bboxs.append(xywh_obj) + confs.append([conf.item()]) + oids.append(int(cls)) + cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) + if check_rect_overlap(bbow_xyxy, rectangle_top_left_back+rectangle_bottom_right_back) : + counter = counter -1 + else: + continue + annotator.box_label(xyxy, label, color=colors(c, True)) +# print(label) + counters.append(counter) + max_counter = max(counters) + min_counter = min(counters) + total_count = sum(counters) + frame_count += len(counters) + if frame_count > 0: + avg_count = total_count / frame_count + else: + avg_count = 0 + + print(f"objects detected {count}") + print(f"Total objects detected: {counter}") + print(f"Max objects detected: {max_counter}") + xywhs = torch.Tensor(xywh_bboxs) + confss = torch.Tensor(confs) + cv2.putText(im0, f'piglets detected: {counter} Max: {max_counter} Avg: {math.ceil(avg_count)} Min: {min_counter}', text_position, font, font_scale, font_color, font_thickness) +# cv2.putText(im0, f'objects detected {count}', (141, 500), font, font_scale, font_color,font_thickness) +# cv2.rectangle(im0, rectangle_top_left, rectangle_bottom_right, (255, 255, 255), 2) + cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) + # outputs = deepsort.update(xywhs, confss, oids, im0) + # if len(outputs) >= 0: + # if check_rect_overlap(bbow_xyxy, rectangle_top_left_back+rectangle_bottom_right_back) or check_rect_overlap(bbow_xyxy, rectangle_top_left+rectangle_bottom_right) : + # counter = counter -1 + # if check_rect_overlap(bbow_xyxy, rectangle_top_left+rectangle_bottom_right): + # count +=1 + # else: + # bbox_xyxy = outputs[:, :4] + # identities = outputs[:, -2] + # object_id = outputs[:, -1] + # cv2.putText(im0, f'piglets detected: {counter} Max: {max_counter} Avg: {math.ceil(avg_count)} Min: {min_counter}', text_position, font, font_scale, font_color, font_thickness) + # cv2.rectangle(im0, rectangle_top_left, rectangle_bottom_right, (255, 255, 255), 2) + # cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) + # draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities) + return log_string + + +@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) +def predict(cfg): + init_tracker() + cfg.model = cfg.model or "yolov8n-seg.pt" + cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size + cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" + + predictor = SegmentationPredictor(cfg) + predictor() + + +if __name__ == "__main__": + predict() From 0eec21482271360e1c811da8b02a4764d5ce70cf Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Mon, 22 May 2023 21:46:29 +0700 Subject: [PATCH 6/7] Update predict_cam2.py --- ultralytics/yolo/v8/segment/predict_cam2.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/ultralytics/yolo/v8/segment/predict_cam2.py b/ultralytics/yolo/v8/segment/predict_cam2.py index 27437ca..06cea71 100644 --- a/ultralytics/yolo/v8/segment/predict_cam2.py +++ b/ultralytics/yolo/v8/segment/predict_cam2.py @@ -256,9 +256,8 @@ def write_results(self, idx, preds, batch): cv2.rectangle(im0, rectangle_top_left_back, rectangle_bottom_right_back, (0, 0, 0), -1) if check_rect_overlap(bbow_xyxy, rectangle_top_left_back+rectangle_bottom_right_back) : counter = counter -1 - else: - continue - annotator.box_label(xyxy, label, color=colors(c, True)) + else: + annotator.box_label(xyxy, label, color=colors(c, True)) # print(label) counters.append(counter) max_counter = max(counters) From cc4d6daa89d2ba0958bfab465c4f7c819fd6acd6 Mon Sep 17 00:00:00 2001 From: Worakan Lasudee <78127819+19xx47@users.noreply.github.com> Date: Thu, 25 May 2023 11:39:48 +0700 Subject: [PATCH 7/7] Update README.md --- README.md | 94 +------------------------------------------------------ 1 file changed, 1 insertion(+), 93 deletions(-) diff --git a/README.md b/README.md index 96e31b7..6ed9dd6 100644 --- a/README.md +++ b/README.md @@ -1,93 +1 @@ -

-YOLOv8 Segmentation with DeepSORT Object Tracking(ID + Trails)

- -## Google Colab File Link (A Single Click Solution) -The google colab file link for yolov8 segmentation and tracking is provided below, you can check the implementation in Google Colab, and its a single click implementation -,you just need to select the Run Time as GPU, and click on Run All. - -[`Google Colab File`](https://colab.research.google.com/drive/1wRkrquf_HMV7tyKy2zDAuqqK9G4zZub5?usp=sharing) - -## YouTube Video Tutorial Link - -[`YouTube Link`](https://www.youtube.com/watch?v=0JIPNk21ivU) - - -## YOLOv8 with DeepSORT Object Tracking - -[`Github Repo Link`](https://github.com/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking.git) - -## Object Segmentation and Tracking (ID + Trails) using YOLOv8 on Custom Data -## Google Colab File Link (A Single Click Solution) -[`Google Colab File`](https://colab.research.google.com/drive/1cnr9Jjj5Pag5myK6Ny8v5gtHgOqf6uoF?usp=sharing) - -## YouTube Video Tutorial Link - -[`YouTube Link`](https://www.youtube.com/watch?v=e-uzr2Sm0DA) - -## Steps to run Code - -- Clone the repository -``` -git clone https://github.com/MuhammadMoinFaisal/YOLOv8_Segmentation_DeepSORT_Object_Tracking.git -``` -- Goto the cloned folder. -``` -cd YOLOv8_Segmentation_DeepSORT_Object_Tracking -``` -- Install the Dependencies -``` -pip install -e '.[dev]' - -``` -- Setting the Directory. -``` -cd ultralytics/yolo/v8/segment - -``` -- Downloading the DeepSORT Files From The Google Drive -``` - -https://drive.google.com/drive/folders/1kna8eWGrSfzaR6DtNJ8_GchGgPMv3VC8?usp=sharing -``` -- After downloading the DeepSORT Zip file from the drive, unzip it go into the subfolders and place the deep_sort_pytorch folder into the ultralytics/yolo/v8/segment folder - -- Downloading a Sample Videos from the Google Drive -- Demo Video 1 -``` -gdown "https://drive.google.com/uc?id=19P9Cf9UiJ9gU9KHnAfud6hrFOgobETTX" -``` - -- Demo Video 2 -``` -gdown "https://drive.google.com/uc?id=1rjBn8Fl1E_9d0EMVtL24S9aNQOJAveR5&confirm=t" -``` -- Demo Video 3 -``` -gdown "https://drive.google.com/uc?id=1aL0XIoOQlHf9FBvUx3FMfmPbmRu0-rF-&confirm=t" -``` -- Run the code with mentioned command below. - -- For yolov8 segmentation + Tracking -``` -python predict.py model=yolov8x-seg.pt source="test1.mp4" -``` - -### RESULTS - -#### Object Segmentation and DeepSORT Tracking (ID + Trails) and Vehicles Counting -![](./ultralytics/figure_speed.png) - -#### Object Segmentation and DeepSORT Tracking (ID + Trails) - -![](./ultralytics/figure2.png) - -### Watch the Complete Step by Step Explanation - -- Video Tutorial Link [`YouTube Link`](https://www.youtube.com/watch?v=0JIPNk21ivU) - - -[![Watch the Complete Tutorial for the Step by Step Explanation](https://img.youtube.com/vi/0JIPNk21ivU/0.jpg)]([https://www.youtube.com/watch?v=0JIPNk21ivU&t=244s](https://www.youtube.com/watch?v=0JIPNk21ivU)) - - -## References -- https://github.com/ultralytics/ultralytics +coming soon