# -*- coding: utf-8 -*- """ Created on Mon Jan 15 15:26:38 2024 @author: ym """ import numpy as np import cv2 import os from pathlib import Path import matplotlib.pyplot as plt from sklearn.decomposition import PCA from tracking.utils.annotator import TrackAnnotator from tracking.utils.plotting import colors def plot_frameID_y2(vts): # boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] # 0, 1, 2, 3, 4, 5, 6, 7, 8 markers = ['o', 'v', '^', '<', '>', 's', 'p', 'P','*', '+', 'x', 'X', 'd', 'D', 'H'] colors = ['b', 'g', 'c', 'm', 'y', ] bboxes = vts.bboxes maxfid = max(vts.bboxes[:, 7]) CART_HIGH_THRESH1 = 430 TRACK_STATIC_THRESH = 8 fig = plt.figure(figsize=(16, 12)) gs = fig.add_gridspec(2, 1, left=0.1, right=0.9, bottom=0.1, top=0.9, wspace=0.05, hspace=0.15) # ax1, ax2 = axs ax1 = fig.add_subplot(gs[0,0]) ax2 = fig.add_subplot(gs[1,0]) ax1.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 ) ax2.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 ) hands = [t for t in vts.Hands if not t.isHandStatic] tracks = vts.join_tracks(vts.Residual, hands) for i, track in enumerate(vts.tracks): boxes = track.boxes cls, tid = track.cls, track.tid y2, fids = boxes[:, 3], boxes[:, 7] if cls==0: ax1.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}") else: ax1.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)], s=50, label = f"ID_{tid}") # hist, bins = np.histogram(1280-y2, bins='auto') ax1.set_ylim([-50, 1350]) for i, track in enumerate(tracks): boxes = track.boxes cls, tid = track.cls, track.tid y2, fids = boxes[:, 3], boxes[:, 7] if cls==0: ax2.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}") else: ax2.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)], s=50, label = f"ID_{tid}") # hist, bins = np.histogram(1280-y2, bins='auto') ax2.set_ylim([-50, 1350]) ax1.grid(True), ax1.set_xlim(0, maxfid+5), ax1.set_title('y2') ax1.legend() ax2.grid(True), ax2.set_xlim(0, maxfid+5), ax2.set_title('y2') ax2.legend() # plt.show() return plt def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False): '''显示四种类型结果''' # file, ext = os.path.splitext(filename) # edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png") # edgeline2 = edgeline1.copy() # edgeline = np.concatenate((edgeline1, edgeline2), exis = 1) if not isinstance(save_dir, Path): save_dir = Path(save_dir) ''' all tracks 中心轨迹''' img1, img2 = edgeline.copy(), edgeline.copy() img1 = drawTrack(vts.tracks, img1) img2 = drawTrack(vts.Residual, img2) imgcat = np.concatenate((img1, img2), axis = 1) H, W = imgcat.shape[:2] cv2.line(imgcat, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2) # imgpth = save_dir.joinpath(f"{file}_show.png") # cv2.imwrite(str(imgpth), img) if not draw5p: return imgcat ''' tracks 5点轨迹''' trackpth = save_dir / Path("trajectory") if not trackpth.exists(): trackpth.mkdir(parents=True, exist_ok=True) for track in vts.tracks: # if track.cls != 0: img = edgeline.copy() img = draw5points(track, img) pth = trackpth.joinpath(f"{file}_{track.tid}_.png") cv2.imwrite(str(pth), img) # for track in vts.Residual: # # if track.cls != 0: # img = edgeline.copy() # img = draw5points(track, img) # pth = trackpth.joinpath(f"{file}_{track.tid}_.png") # cv2.imwrite(str(pth), img) return imgcat # ============================================================================= # '''3. moving tracks 中心轨迹''' # filename2 = f"{file}_show_r.png" # img = edgeline.copy() # img = drawTrack(vts.Residual, img) # pth = save_dir.joinpath(filename2) # cv2.imwrite(pth, img) # ============================================================================= '''5. tracks 时序：trajmin、trajmax、arearate、incartrate''' # plt = drawtracefeat(vts) # pth = save_dir.joinpath(f"{file}_x.png") # plt.savefig(pth) # plt.close('all') def drawFeatures(allvts, save_dir): # [trajlen_min, trajdist_max, trajlen_rate, trajist_rate]] feats = [track.TrajFeat for vts in allvts for track in vts.tracks] feats = np.array(feats) fig, ax = plt.subplots() ax.scatter(feats[:,3], feats[:, 1], s=10) # ax.set_xlim(0, 2) # ax.set_ylim(0, 100) ax.grid(True) plt.show() pth = save_dir.joinpath("scatter.png") plt.savefig(pth) plt.close('all') def drawtracefeat(vts): ''' 需要对曲线进行特征提取和分类 boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 0 1 2 3 4 5 6 7 8 ''' # matplotlib.use('Agg') fid = vts.frameid fid1, fid2 = min(fid), max(fid) fig, axs = plt.subplots(2, 2,figsize=(18, 8)) kernel = [0.15, 0.7, 0.15] for i, track in enumerate(vts.tracks): boxes = track.boxes tid = int(track.tid) cls = int(track.cls) posState = track.posState if track.frnum>=5: x1 = boxes[1:, 7] y1 = track.trajmin x11 = [i for i in range(int(min(x1)), int(max(x1)+1))] y11 = np.interp(x11, x1, y1) y11[1:-1] = np.convolve(y11, kernel, 'valid') x3 = boxes[1:, 7] y3 = track.trajmax x33 = [i for i in range(int(min(x3)), int(max(x3)+1))] y33 = np.interp(x33, x3, y3) y33[1:-1] = np.convolve(y33, kernel, 'valid') x2 = boxes[:, 7] # y2 = track.Area/max(track.Area) - min(track.Area/max(track.Area)) y2 = track.Area/max(track.Area) x22 = [i for i in range(int(min(x2)), int(max(x2)+1))] y22 = np.interp(x22, x2, y2) y22[1:-1] = np.convolve(y22, kernel, 'valid') x4 = boxes[:, 7] y4 = track.incartrates x44 = [i for i in range(int(min(x4)), int(max(x4)+1))] y44 = np.interp(x44, x4, y4) y44[1:-1] = np.convolve(y44, kernel, 'valid') elif track.frnum>=2: x11 = boxes[1:, 7] y11 = track.trajmin x33 = boxes[1:, 7] y33 = track.trajmax x22 = boxes[:, 7] # y22 = track.Area/max(track.Area) - min(track.Area/max(track.Area)) y22 = track.Area/max(track.Area) x44 = boxes[:, 7] y44 = track.incartrates else: continue # cls!=0, max(y)>20 if cls!=0 and cls!=9 and posState>=2 and max(y11)>10 and max(y33)>10 and max(y22>0.1): axs[0, 0].plot(x11, y11, label=f"ID_{tid}") axs[0, 0].legend() # axs[0].set_ylim(0, 100) axs[0, 1].plot(x22, y22, label=f"ID_{tid}") axs[0, 1].legend() axs[1, 0].plot(x33, y33, label=f"ID_{tid}") axs[1, 0].legend() axs[1, 1].plot(x44, y44, label=f"ID_{tid}") axs[1, 1].legend() axs[0, 0].grid(True), axs[0, 0].set_xlim(fid1, fid2+10), axs[0, 0].set_title('trajmin') axs[0, 1].grid(True), axs[0, 1].set_xlim(fid1, fid2+10), axs[0, 1].set_title('arearate') axs[1, 0].grid(True), axs[1, 0].set_xlim(fid1, fid2+10), axs[1, 0].set_title('trajmax') axs[1, 1].grid(True), axs[1, 1].set_xlim(fid1, fid2+10), axs[1, 1].set_ylim(-0.1, 1.1) axs[1, 1].set_title('incartrate') # pth = save_dir.joinpath(f"{file}_show_x.png") # plt.savefig(pth) # plt.savefig(f"./result/cls11_80212_time/{file}_show_x.png") # plt.show() return plt def draw5points(track, img): """ 显示中心点、4角点的轨迹，以及轨迹 features """ colorx = np.array([[255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [0, 0, 255], [0, 255, 0], [255, 51, 255], [102, 178, 255], [51, 153, 255],[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51], [255, 102, 255], [153, 204, 255], [255, 0, 0], [255, 255, 255]], dtype=np.uint8) color = ((0, 0, 255), (255, 128, 0)) # img = cv2.imread("./shopcart/cart_tempt/edgeline.png") boxes = track.boxes cornpoints = track.cornpoints trajlens = [int(t) for t in track.trajlens] trajdist = [int(t) for t in track.trajdist] if len(track.trajmin): trajstd = np.std(track.trajmin) else: trajstd = 0 trajlen_min, trajlen_max, trajdist_min, trajdist_max, trajlen_rate, trajdist_rate = track.TrajFeat for i in range(boxes.shape[0]): cv2.circle(img, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 6, (255, 255, 255), 2) cv2.circle(img, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), 6, (255, 0, 255), 2) cv2.circle(img, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), 6, (0, 255, 0), 2) cv2.circle(img, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), 6, (64, 128, 255), 2) cv2.circle(img, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), 6, (255, 128, 64), 2) label_0 = f"ID: {track.tid}, Class: {track.cls}" label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}" label_2 = f"trajdist: {trajdist}: trajdist_max: {int(trajdist_max)}" label_3 = "trajlen_min/trajlen_max: {:.2f}/{:.2f} = {:.2f}".format(trajlen_min, trajlen_max, trajlen_rate) label_4 = "trajdist_min/mwh : {:.2f}/{:.2f} = {:.2f}".format(trajdist_min, track.mwh, trajdist_rate) label_5 = "std(trajmin) : {:.2f}".format(trajstd) label_6 = "PCA(variance_ratio) : " label_7 = "Rect W&H&Ratio : " label_8 = "" # label_8 = "IOU of incart/maxbox/minbox: {:.2f}, {:.2f}, {:.2f}".format( # track.feature_ious[0], track.feature_ious[3], track.feature_ious[4]) '''=============== 最小轨迹长度索引 ====================''' trajlens = [int(t) for t in track.trajrects_wh] if track.isCornpoint: idx = 0 else: idx = trajlens.index(min(trajlens)) '''=============== PCA ====================''' if trajlens[idx] > 12: X = cornpoints[:, 2*idx:2*(idx+1)] pca = PCA() pca.fit(X) label_6 = "PCA(variance_ratio): {:.2f}".format(pca.explained_variance_ratio_[0]) # if sum(np.isnan(pca.explained_variance_ratio_)) == 0: for i, (comp, var) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)): pt1 = (pca.mean_ - comp*var*200).astype(np.int64) pt2 = (pca.mean_ + comp*var*200).astype(np.int64) cv2.line(img, pt1, pt2, color=color[i], thickness=2) '''=============== RECT ====================''' rect = track.trajrects[idx] box = cv2.boxPoints(rect) box = np.int0(box) cv2.drawContours(img, [box], 0, (0, 255, 0), 2) label_7 = "Rect W&H&Ratio: {}, {}, {:.2f}".format(int(rect[1][0]), int(rect[1][1]), min(rect[1])/(max(rect[1])+0.001)) '''=============== 显示文字 ====================''' # label = [label_0, label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8] # w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0] # for i in range(len(label)): # cv2.putText(img, label[i], (20, int((i+1)*1.1*h)), 0, 1, # [int(x) for x in colorx[i]], 2, lineType=cv2.LINE_AA) # pth = save_dir.joinpath(f"{file}_{track.tid}.png") # cv2.imwrite(pth, img) '''撰写专利需要，生成黑白图像''' # imgbt = cv2.bitwise_not(img) # for i in range(box.shape[0]): # cv2.circle(imgbt, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 14, (0, 0, 0), 2) # cv2.drawMarker(imgbt, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), color= (0, 0, 0), markerType=3, markerSize = 30, thickness=2) # cv2.drawMarker(imgbt, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), color= (0, 0, 0), markerType=4, markerSize = 30, thickness=2) # cv2.drawMarker(imgbt, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), color= (0, 0, 0), markerType=5, markerSize = 30, thickness=2) # cv2.drawMarker(imgbt, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), color= (0, 0, 0), markerType=6, markerSize = 30, thickness=2) # cv2.imwrite(pth + f"/zhuanli/{file}_{track.tid}.png", imgbt) return img def drawTrack(tracks, img): # img = cv2.imread("./shopcart/cart_tempt/edgeline.png") annotator = TrackAnnotator(img, line_width=2) for track in tracks: annotator.plotting_track(track.boxes) img = annotator.result() # pth = save_dir.joinpath(f"{filename}") # cv2.imwrite(pth, img) return img if __name__ == "__main__": y = np.array([5.0, 20, 40, 41, 42, 55, 56])