'''
@File : infer.py
@Time : 2024/10/17 11:18:00
@Author : leon
@Version : 1.0
@Desc : None
'''
import cv2
import torch
import numpy as np
from ultralytics import YOLO
from shapely.geometry.polygon import Polygon
from collections import namedtuple
from typing import List, Tuple
Box = namedtuple("Box", ["left", "top", "right", "bottom", "confidence", "label"])
class PerspectiveMatrix:
def __init__(self, matrix: np.ndarray, target: Tuple[int, int]) -> None:
self.matrix = matrix
self.target = target
def __repr__(self):
matrix_str = np.array2string(self.matrix, formatter={'float_kind': lambda x: f"{x:.2f}"})
return f"PerspectiveMatrix(matrix={matrix_str}, target={self.target})"
@staticmethod
def perspective_matrix(src: List[Tuple[int, int]], dst: List[Tuple[int, int]], target : Tuple[int, int]) -> "PerspectiveMatrix":
"""
计算透视变换矩阵。
参数:
- src: 源图像上的 4 个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)]
- dst: 目标图像上的 4 个点 [(u1, v1), (u2, v2), (u3, v3), (u4, v4)]
返回:
- PerspectiveMatrix: 透视变换矩阵
"""
src_pts = np.array(src, dtype=np.float32)
dst_pts = np.array(dst, dtype=np.float32)
# 计算透视变换矩阵
# matrix = cv2.getPerspectiveTransform(src_pts, dst_pts)
# 单应性矩阵
matrix, _ = cv2.findHomography(src_pts, dst_pts)
return PerspectiveMatrix(matrix=matrix, target=target)
class ImageTransformer:
@staticmethod
def PerspectiveTransform(
image: np.ndarray,
perspective_matrix: PerspectiveMatrix,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(114, 114, 114)
) -> np.ndarray:
if image is None or perspective_matrix is None:
raise ValueError("Input image and warpaffine_matrix cannot be None.")
transformed_image = cv2.warpPerspective(
image,
perspective_matrix.matrix,
perspective_matrix.target,
flags=flags,
borderMode=borderMode,
borderValue=borderValue)
return transformed_image
class AreaManage(object):
def __init__(self):
self.door_areas = [
# [(934, 143), (958, 130), (961, 165), (936, 181)],
[(564, 399), (682, 432), (684, 528), (574, 493)]]
self.door_perspective_matrix = []
self.dst_door_points = [(0,0), (224, 0), (224, 224), (0, 224)]
for p in self.door_areas:
perspective_matrix = PerspectiveMatrix.perspective_matrix(p, self.dst_door_points, (224, 224))
self.door_perspective_matrix.append(perspective_matrix)
# 预设值的三个门对应的人的活动区域
self.person_areas = [
# [(900, 152), (914, 84), (637, 20), (604, 73)],
[(860, 200), (958, 226), (687, 432), (586, 395)]]
self.person_areas_polygon = [Polygon(area) for area in self.person_areas]
def update(self, person_area):
areas = []
person_area_polygon = Polygon(person_area)
for pap in self.person_areas_polygon:
areas.append(person_area_polygon.intersection(pap).area)
max_area = max(areas)
if max_area > 0:
max_idx = areas.index(max_area)
self.person_areas_polygon[max_idx] = person_area_polygon
def inner_area(self, person_polygon):
for i in range(len(self.person_areas_polygon)):
overlap_ratop = person_polygon.intersection(self.person_areas_polygon[i]).area / person_polygon.area
if overlap_ratop > 0.4:
return i
return -1
"""
1. 人员检测
2. 检查区域内是否有人
3. 如果区域内有人，分类该区域的门是否关闭
4. 如果关闭，上报违章
"""
class DoorInference(object):
"""
human_model_path : 检测人的模型地址
door_model_path : 门分类的模型地址
person_areas : 电子围栏区域 [[(x,y),(x,y),(x,y),(x,y),...],[(x,y),(x,y),(x,y),(x,y),...],...]
device_id : 显卡id, -1表示使用cpu
confidence_threshold : 检测人的模型的阈值
"""
def __init__(self, human_model_path, door_model_path, person_areas, device_id=0, confidence_threshold= 0.5) -> "DoorInference":
self.device = torch.device(f"cuda:{device_id}" if torch.cuda.is_available() and device_id !=-1 else "cpu")
self.confidence_threshold = confidence_threshold
self.human_model = YOLO(human_model_path).to(self.device)
self.door_model = YOLO(door_model_path).to(self.device)
self.door_names = {0: 'block', 1: 'close', 2: 'open'}
self.am = AreaManage()
if person_areas:
for person_area in person_areas:
self.am.update(person_area)
def __repr__(self):
infer_str = f"DoorInference(device = {self.device})"
return infer_str
def rect(self, point_list):
"""
根据给定的点计算矩形框
:param point_list: [(x1, y1), (x2, y2), ..., (xn, yn)] 多个点
:return: 左上角和右下角的坐标，表示矩形的框
"""
# 获取所有点的 x 和 y 坐标的最小和最大值
min_x = min(point[0] for point in point_list)
max_x = max(point[0] for point in point_list)
min_y = min(point[1] for point in point_list)
max_y = max(point[1] for point in point_list)
# 计算矩形框的左上角和右下角
left, top = min_x, min_y
right, bottom = max_x, max_y
return left, top ,right, bottom
"""
返回所有检测到的人
"""
def person_detect(self, image):
objs = []
confs = []
results = self.human_model(image, stream=False, classes=[0], conf=self.confidence_threshold, iou=0.3, imgsz=640)
for result in results:
boxes = result.boxes.cpu()
for box in boxes:
conf = box.conf.cpu().numpy().tolist()[0]
left, top, right, bottom = box.xyxy.tolist()[0]
objs.append([(left, top), (right, top), (right, bottom), (left, bottom)])
confs.append(conf)
return objs, confs
"""
判断门是否关闭
"""
def is_door_close(self, image, index) -> bool:
# 图片透视变换, 只需要门的区域
# 比只使用矩形框更大程度的只保留门的区域
door_image = ImageTransformer.PerspectiveTransform(image, self.am.door_perspective_matrix[index])
# cv2.imshow('0',door_image)
# cv2.waitKey(30000)
res = self.door_model(door_image)
class_idx = res[0].probs.top1
class_conf = res[0].probs.top1conf.cpu().numpy().item()
return class_idx == 1, class_conf
"""
image 输入待识别图片
返回需要画框的门和人的坐标
"""
def __call__(self, image):
inner_person = {0:[], 1:[], 2:[]}
person_boxes, person_confs = self.person_detect(image)
for person_box, person_conf in zip(person_boxes, person_confs):
person_polygon = Polygon(person_box)
idx = self.am.inner_area(person_polygon)
if idx == -1: continue
inner_person[idx].append({"box" : person_box, "conf" : person_conf})
result = []
for i, persons in inner_person.items():
if len(persons) == 0:
continue
close, class_conf = self.is_door_close(image, i)
if close:
left, top ,right, bottom = self.rect(self.am.door_areas[i])
result.append(Box(left=left, top=top, right=right, bottom=bottom, confidence=class_conf, label="door_close"))
for person in persons:
left, top ,right, bottom = self.rect(person["box"])
conf = person["conf"]
result.append(Box(left=left, top=top, right=right, bottom=bottom, confidence=conf, label="person"))
return result