362 lines
12 KiB
Python
362 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Wed Jul 5 10:01:11 2023
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@author: ym
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"""
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import numpy as np
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import os
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import cv2
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import sys
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from scipy.spatial.distance import cdist
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import matplotlib
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import matplotlib.pyplot as plt
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from sklearn.decomposition import PCA
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# from ultralytics.utils.plotting import Annotator, colors
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from .annotator import TrackAnnotator
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# from .processboxes import array2list
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# boxes Format: [x1, y1, x2, y2, track_id, score, cls, frame_index]
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pth = r"D:/DeepLearning/yolov5/tracking/"
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colors = np.array([[255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255],
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[0, 0, 255], [0, 255, 0], [255, 51, 255], [102, 178, 255], [51, 153, 255],
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[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
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[51, 255, 51], [255, 102, 255], [153, 204, 255], [255, 0, 0], [255, 255, 255]],
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dtype=np.uint8)
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def array2list(bboxes):
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'''
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将 bboxes 变换为 track 列表
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bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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Return：
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lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
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[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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'''
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tids = set(bboxes[:, 4])
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track_ids = bboxes[:, 4].astype(int)
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lboxes = []
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for t_id in tids:
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# print(f"The ID is: {t_id}")
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idx = np.where(track_ids == t_id)[0]
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box = bboxes[idx, :]
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lboxes.append(box)
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return lboxes
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def draw5points(bboxes, file):
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"""
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显示中心点、4角点的轨迹，以及轨迹 features
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"""
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image = cv2.imread(pth + r"/shopcart/cart_tempt/edgeline.png")
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imgx = image.copy()
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annotator = TrackAnnotator(imgx, line_width=2)
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lboxes = array2list(bboxes)
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for k in range(len(lboxes)):
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boxes = lboxes[k]
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cls = int(boxes[0, 6])
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tid = int(boxes[0, 4])
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# print(tid)
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frnum = boxes.shape[0]
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cornpoints = np.zeros((frnum, 10))
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cornpoints[:,0] = (boxes[:, 0] + boxes[:, 2]) / 2
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cornpoints[:,1] = (boxes[:, 1] + boxes[:, 3]) / 2
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cornpoints[:,2], cornpoints[:,3] = boxes[:, 0], boxes[:, 1]
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cornpoints[:,4], cornpoints[:,5] = boxes[:, 2], boxes[:, 1]
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cornpoints[:,6], cornpoints[:,7] = boxes[:, 0], boxes[:, 3]
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cornpoints[:,8], cornpoints[:,9] = boxes[:, 2], boxes[:, 3]
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x1, y1, x2, y2 = cornpoints[:,2],cornpoints[:,3],cornpoints[:,8],cornpoints[:,9]
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BoundPixel = 10
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BoundThresh = 0.4
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cont1 = sum(abs(x1)<BoundPixel) / frnum > BoundThresh
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cont2 = sum(abs(y1)<BoundPixel) / frnum > BoundThresh
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cont3 = sum(abs(x2-1024)<BoundPixel) / frnum > BoundThresh
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cont4 = sum(abs(y2-1280)<BoundPixel) / frnum > BoundThresh
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isImgBorder = False
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if cont1 or cont2 or cont3 or cont4:
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isImgBorder = True
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# =============================================================================
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# '''情况1: 在商品运动过程中，商品检测框始终左下角点和图像左下角点重合, 用中心点代替'''
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# lfcn_dist = np.linalg.norm(cornpoints[:, 6:8] - [0, 1280], axis=1)
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# idx1 = lfcn_dist<10
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# if sum(idx1)/frnum > 0.5:
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# cornpoints[:, 6:8] = cornpoints[:, 0:2]
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#
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# '''情况2: 在商品运动过程中，商品检测框始终右下角点和图像右下角点重合, 用中心点代替'''
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# rtcn_dist = np.linalg.norm(cornpoints[:, 8:10] - [1024, 1280], axis=1)
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# idx2 = rtcn_dist<10
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# if sum(idx2)/frnum > 0.5:
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# cornpoints[:, 8:10] = cornpoints[:, 0:2]
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# =============================================================================
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mwh = (np.mean(boxes[:, 2]) + np.mean(boxes[:, 3]))/2
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trajectory = []
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trajlens = []
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trajdist = []
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for k in range(5):
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traj = np.linalg.norm(np.diff(cornpoints[:, 2*k:2*(k+1)], axis = 0), axis=1)
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trajlen = np.sum(traj)
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ptdist = np.max(cdist(cornpoints[:, 2*k:2*(k+1)], cornpoints[:, 2*k:2*(k+1)]))
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trajectory.append(traj)
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trajlens.append(trajlen)
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trajdist.append(ptdist)
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if not isImgBorder:
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idx = trajlens.index(min(trajlens))
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trajmin = trajectory[idx]
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trajlen_min = min(trajlens)
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trajdist_min = min(trajdist)
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else:
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trajmin = trajectory[0]
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trajlen_min = trajlens[0]
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trajdist_min = trajdist[0]
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'''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
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trajlen_rate = trajlen_min/(max(trajlens)+0.0001)
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'''最小轨迹欧氏距离/目标框尺度均值'''
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trajdist_rate = trajdist_min/(mwh+0.0001)
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# idx = trajlens.index(min(trajlens))
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# trajmin = trajectory[idx]
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# '''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
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# trajlen_rate = min(trajlens)/(max(trajlens)+0.0001)
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# '''最小轨迹欧氏距离，越小，代表运动幅度越小'''
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# trajdist_min = min(trajdist)
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# '''最小轨迹欧氏距离 / 目标框尺度均值'''
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# mindist_rate = min(trajdist)/(mwh+0.0001)
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img = image.copy()
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for i in range(boxes.shape[0]):
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cv2.circle(img, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 6, (255, 255, 255), 2)
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cv2.circle(img, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), 6, (255, 0, 255), 2)
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cv2.circle(img, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), 6, (0, 255, 0), 2)
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cv2.circle(img, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), 6, (64, 128, 255), 2)
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cv2.circle(img, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), 6, (255, 128, 64), 2)
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# if frnum>=3:
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# cntpoints = cornpoints[:, 0:2].astype(np.int64)
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# rect = cv2.minAreaRect(cntpoints)
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# box = cv2.boxPoints(rect)
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# box = np.int0(box)
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# cv2.drawContours(img, [box], 0, (255, 0, 0), 2)
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# img1 = image.copy()
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# for i in range(boxes.shape[0]-1):
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# pt1 = cornpoints[i, :].astype(np.int64)
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# pt2 = cornpoints[i+1, :].astype(np.int64)
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# cv2.line(img1, pt1, pt2, color=(255, 255, 255), thickness=2)
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# gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
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# _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
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# contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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color = ((0, 0, 255), (255, 128, 0))
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label_6 = "PCA(singular_values_) : "
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label_7 = "Rect : "
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if frnum>=3:
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if isImgBorder:
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X = cornpoints[:, 0:2]
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else:
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X = cornpoints[:, 2*idx:2*(idx+1)]
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pca = PCA()
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pca.fit(X)
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label_6 = "PCA(variance_ratio) : {:.2f}".format(pca.explained_variance_ratio_[0])
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for i, (comp, var) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
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pt1 = (pca.mean_ - comp*var*200).astype(np.int64)
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pt2 = (pca.mean_ + comp*var*200).astype(np.int64)
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cv2.line(img, pt1, pt2, color=color[i], thickness=2)
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rect = cv2.minAreaRect(X.astype(np.int64))
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
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label_7 = "Rect W&H&Ratio: {}, {}, {:.2f}".format(int(rect[1][0]), int(rect[1][1]), min(rect[1])/(max(rect[1])+0.001))
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'''撰写专利需要，生成黑白图像'''
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# imgbt = cv2.bitwise_not(img)
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# for i in range(box.shape[0]):
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# cv2.circle(imgbt, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 14, (0, 0, 0), 2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), color= (0, 0, 0), markerType=3, markerSize = 30, thickness=2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), color= (0, 0, 0), markerType=4, markerSize = 30, thickness=2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), color= (0, 0, 0), markerType=5, markerSize = 30, thickness=2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), color= (0, 0, 0), markerType=6, markerSize = 30, thickness=2)
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# cv2.imwrite(pth + f"/zhuanli/{file}_{tid}.png", imgbt)
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if len(trajmin):
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trajstd = np.std(trajmin)
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else:
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trajstd = 0
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trajlens = [int(t) for t in trajlens]
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trajdist = [int(t) for t in trajdist]
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label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}"
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label_2 = f"trajdist: {trajdist}: trajdist_min: {int(trajdist_min)}"
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label_3 = "trajlen_min/max(trajlens): {:.2f}/{} = {:.2f}".format(trajlen_min, max(trajlens), trajlen_rate)
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label_4 = "trajdist_min/mwh : {:.2f}/{} = {:.2f}".format(trajdist_min, int(mwh), trajdist_rate)
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label_5 = "std(trajmin) : {:.2f}".format(trajstd)
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label = [label_1, label_2, label_3, label_4, label_5, label_6, label_7]
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word = 'abc'
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w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0]
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for i in range(len(label)):
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# color = [int(x) for x in colors[i]]
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cv2.putText(img,
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label[i],
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(20, int(50+(i+1)*1.2*h)),
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0,
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1,
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[int(x) for x in colors[i]],
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2,
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lineType=cv2.LINE_AA)
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cv2.imwrite(pth + f"/result/cls11_80212/{file}_{tid}.png", img)
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def drawtracks(bboxes, imgshow=None):
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"""
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Inputs
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bboxes: 原始检测跟踪后的结果，变换为 tboxes
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image：只用于获取图像的（Width, Height）
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Outputs:
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imgshow
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"""
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if imgshow is None:
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edgeline = cv2.imread(pth + r"/shopcart/cart_tempt/edgeline.png")
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# edgeline = cv2.bitwise_not(edgeline)
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H, W = edgeline.shape[0:2]
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imgshow= np.zeros((H, W, 3), np.uint8)
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if 'edgeline' in locals().keys():
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imgshow = cv2.add(imgshow, edgeline)
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## ==== list，其中元素格式: [x, y, w, h, track_id, score, cls, frame_index]
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tboxes = array2list(bboxes)
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# imgshow = cv2.bitwise_not(imgshow)
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annotator = TrackAnnotator(imgshow, line_width=2)
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for boxes in tboxes:
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annotator.plotting_track(boxes)
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imgshow = annotator.result()
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return imgshow
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def writefilename():
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npydir = r"D:\DeepLearning\yolov5\runs\boxes"
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files = [f.split('.')[0] for f in os.listdir(npydir)]
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with open('data.txt', 'w') as f:
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[f.write(f"{file}:\n") for file in files]
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print("len(files)")
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# for filename in os.listdir(npydir):
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# file, ext = os.path.splitext(filename)
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def main():
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npydir = r"D:\DeepLearning\yolov5\runs\boxes"
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k = 0
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fields = []
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for filename in os.listdir(npydir):
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# filename = "加购_快速置入_12.npy"
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print(filename)
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file, ext = os.path.splitext(filename)
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filepath = os.path.join(npydir, filename)
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try:
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bboxes = np.load(filepath)
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imgshow = drawtracks(bboxes, file)
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draw5points(bboxes, file)
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cv2.imwrite(pth + f"/result/cls11_80212/{file}_show.png", imgshow)
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except Exception as e:
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# print(str(e))
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pass
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# k += 1
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# if k == 1:
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# break
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if __name__ == "__main__":
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main()
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# writefilename()
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