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Python+Yolov8入口人流量统计
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前言
这篇博客针对<Python+Yolov8入口人流量统计>编写代码，代码整洁，规则，易读。 学习与应用推荐首选。
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2. 代码实现
3. 运行结果
三、在线协助
一、所需工具软件
1. Python，Pycharm
2. Yolov5
二、使用步骤
1.引入库
import hydra import torch import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random from ultralytics.yolo.engine.predictor import BasePredictor from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops from ultralytics.yolo.utils.checks import check_imgsz from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box import cv2 from deep_sort_pytorch.utils.parser import get_config from deep_sort_pytorch.deep_sort import DeepSort from collections import deque import numpy as np
2. 代码实现
代码如下:
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_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 draw_border(img, pt1, pt2, color, thickness, r, d): x1,y1 = pt1 x2,y2 = pt2 # Top left cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness) cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness) cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness) # Top right cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness) cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness) cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness) # Bottom left cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness) cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness) cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness) # Bottom right cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness) cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness) cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness) cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA) cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA) cv2.circle(img, (x1 +r, y1+r), 2, color, 12) cv2.circle(img, (x2 -r, y1+r), 2, color, 12) cv2.circle(img, (x1 +r, y2-r), 2, color, 12) cv2.circle(img, (x2 -r, y2-r), 2, color, 12) return img 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 = draw_border(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color, 1, 8, 2) cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) def intersect(A,B,C,D): return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D) def ccw(A,B,C): return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0]) def get_direction(point1, point2): direction_str = "" # calculate y axis direction if point1[1] > point2[1]: direction_str += "South" elif point1[1] < point2[1]: direction_str += "North" else: direction_str += "" # calculate x axis direction if point1[0] > point2[0]: direction_str += "East" elif point1[0] < point2[0]: direction_str += "West" else: direction_str += "" return direction_str def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)): cv2.line(img, line[0], line[1], (0,0,255), 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) if len(data_deque[id]) >= 2: direction = get_direction(data_deque[id][0], data_deque[id][1]) if intersect(data_deque[id][0], data_deque[id][1], line[0], line[1]): cv2.line(img, line[0], line[1], (255, 255, 255), 3) if "South" in direction: if obj_name not in object_counter: object_counter[obj_name] = 1 else: object_counter[obj_name] += 1 if "North" in direction: if obj_name not in object_counter1: object_counter1[obj_name] = 1 else: object_counter1[obj_name] += 1 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) #4. Display Count in top right corner for idx, (key, value) in enumerate(object_counter1.items()): cnt_str = str(key) + ":" +str(value) #cv2.line(img, (width - 500,25), (width,25), [85,45,255], 40) cv2.putText(img, f'Entering', (width - 500, 35), 0, 1, [0,0,255], thickness=2, lineType=cv2.LINE_AA) #cv2.line(img, (width - 500, 65 + (idx*40)), (width, 65 + (idx*40)), [85, 45, 255], 30) cv2.putText(img, cnt_str, (width - 500, 75 + (idx*40)), 0, 1, [0,0,255], thickness = 2, lineType = cv2.LINE_AA) for idx, (key, value) in enumerate(object_counter.items()): cnt_str1 = str(key) + ":" +str(value) #cv2.line(img, (20,25), (500,25), [85,45,255], 40) cv2.putText(img, f'Leaving', (11, 35), 0, 1, [0,0,255], thickness=2, lineType=cv2.LINE_AA) #cv2.line(img, (20,65+ (idx*40)), (127,65+ (idx*40)), [85,45,255], 30) cv2.putText(img, cnt_str1, (11, 75+ (idx*40)), 0, 1, [0,0,255], thickness=2, lineType=cv2.LINE_AA) return img class DetectionPredictor(BasePredictor): def get_annotator(self, img): return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names)) def preprocess(self, img): img = torch.from_numpy(img).to(self.model.device) img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 img /= 255 # 0 - 255 to 0.0 - 1.0 return img def postprocess(self, preds, img, orig_img): preds = ops.non_max_suppression(preds, self.args.conf, self.args.iou, agnostic=self.args.agnostic_nms, max_det=self.args.max_det) for i, pred in enumerate(preds): shape = orig_img[i].shape if self.webcam else orig_img.shape pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() return preds def write_results(self, idx, preds, batch): p, im, im0 = batch all_outputs = [] log_string = "" if len(im.shape) == 3: im = im[None] # expand for batch dim self.seen += 1 im0 = im0.copy() 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 save_path = str(self.save_dir / p.name) # im.jpg 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) det = preds[idx] all_outputs.append(det) if len(det) == 0: return log_string 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)}, " # write gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh xywh_bboxs = [] confs = [] oids = [] outputs = [] for *xyxy, conf, cls in reversed(det): 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.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" cfg.source = "imagesVideo/test2.mp4" predictor = DetectionPredictor(cfg) predictor() if __name__ == "__main__": predict()
3. 运行结果
三、在线协助：
如需安装运行环境或远程调试， 可点击右边 博主头像 或 昵称 ， 进入个人主页查看博主联系方式 ，由专业技术人员远程协助！
1）远程安装运行环境，代码调试
2）Qt, C++, Python入门指导
3）界面美化
4）软件制作
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