pipeline.py 等更新
This commit is contained in:
王庆刚
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@ -169,9 +169,6 @@ def calc_simil(event, stdfeat):
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Similar = None # 在event.front_feats和event.back_feats同时为空时
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return Similar
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def simi_matrix():
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@ -61,8 +61,9 @@ class Config:
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test_val = "D:/比对/cl"
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# test_val = "./data/test_data_100"
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test_model = "checkpoints/best_resnet18_v12.pth"
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# test_model = "checkpoints/best_resnet18_v12.pth"
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# test_model = "checkpoints/zhanting_res_801.pth"
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test_model = "checkpoints/zhanting_res_abroad_8021.pth"
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@ -61,8 +61,17 @@ class FeatsInterface:
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batch_patches = []
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patches = []
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for i, img in enumerate(images):
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img = img.copy()
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patch = self.transform(img)
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img = img.copy()
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## 对 img 进行补黑边，生成新的图像new_img
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width, height = img.size
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new_size = max(width, height)
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new_img = Image.new("RGB", (new_size, new_size), (0, 0, 0))
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paste_x = (new_size - width) // 2
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paste_y = (new_size - height) // 2
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new_img.paste(img, (paste_x, paste_y))
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patch = self.transform(new_img)
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if str(self.device) != "cpu":
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patch = patch.to(device=self.device).half()
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else:
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@ -107,10 +116,12 @@ class FeatsInterface:
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patch = self.transform(img1)
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# patch = patch.to(device=self.device).half()
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if str(self.device) != "cpu":
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patch = patch.to(device=self.device).half()
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else:
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patch = patch.to(device=self.device)
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# if str(self.device) != "cpu":
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# patch = patch.to(device=self.device).half()
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# patch = patch.to(device=self.device)
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# else:
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# patch = patch.to(device=self.device)
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patch = patch.to(device=self.device)
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patches.append(patch)
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if (d + 1) % self.batch_size == 0:
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@ -158,6 +158,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
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feature /= np.linalg.norm(feature, axis=1)[:, None]
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feature_ft32 = feature.astype(np.float32)
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# float16
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feature_ft16 = feature.astype(np.float16)
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feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None]
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@ -173,7 +175,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
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##================== float32
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stdbDict["barcode"] = barcode
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stdbDict["imgpaths"] = imgpaths
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stdbDict["feats_ft32"] = feature
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stdbDict["feats_ft32"] = feature_ft32
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stdbDict["feats_ft16"] = feature_ft16
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stdbDict["feats_uint8"] = feature_uint8
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@ -202,9 +204,13 @@ def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None):
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stdfeat_infer(bcdpath, featpath, bcdSet)
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def main():
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imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
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bcdpath = r"D:\exhibition\dataset\bcdpath"
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featpath = r"D:\exhibition\dataset\feats"
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imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
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bcdpath = r"D:\exhibition\dataset\bcdpath_abroad"
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featpath = r"D:\exhibition\dataset\feats_abroad"
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if not os.path.exists(bcdpath):
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os.makedirs(bcdpath)
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if not os.path.exists(featpath):
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os.makedirs(featpath)
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gen_bcd_features(imgpath, bcdpath, featpath)
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@ -92,9 +92,9 @@ def simi_calc(event, o2nevt, typee=None):
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feat2 = o2nevt.front_feats
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'''自定义事件特征选择'''
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if typee==3:
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feat1 = event.feats_compose
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feat2 = o2nevt.feats_compose
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if typee==3 and len(event.feats_compose) and len(o2nevt.feats_compose):
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feat1 = [event.feats_compose]
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feat2 = [o2nevt.feats_compose]
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if len(feat1) and len(feat2):
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@ -109,15 +109,18 @@ def one2n_pr(evtDicts, pattern=1):
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'''
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pattern:
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1: process.data 中记录的相似度
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2: 根据 process.data 中标记的 type 选择特征计算
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3: 以其它方式选择特征计算
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2: 根据 process.data 中标记的 type 选择特征计算相似度
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3: 以其它方式选择特征计算相似度
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'''
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tpevents, fnevents, fpevents, tnevents = [], [], [], []
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tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
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errorFile_one2n = []
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one2nFile, errorFile_one2n = [], []
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for evtname, event in evtDicts.items():
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evt_names, evt_barcodes, evt_similars, evt_types = [], [], [], []
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if len(event.barcode)==0:
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continue
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for ndict in event.one2n:
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nname = ndict["event"]
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@ -149,6 +152,13 @@ def one2n_pr(evtDicts, pattern=1):
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continue
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evt_similars.append(simival)
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## process.data的oneTon的各项中，均不包括当前事件的barcode
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if event.barcode not in evt_barcodes:
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errorFile_one2n.append(evtname)
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continue
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else:
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one2nFile.append(evtname)
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if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
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and len(evt_similars)==len(evt_types) and len(evt_names)>0:
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@ -166,7 +176,7 @@ def one2n_pr(evtDicts, pattern=1):
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elif bcd!=event.barcode and simi!=maxsim:
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tnsimi.append(simi)
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tnevents.append(evtname)
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elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes:
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elif bcd!=event.barcode and simi==maxsim:
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fpsimi.append(simi)
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fpevents.append(evtname)
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else:
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@ -187,7 +197,7 @@ def one2n_pr(evtDicts, pattern=1):
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TN = sum(np.array(tnsimi) < th)
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PPrecise.append(TP/(TP+FP+1e-6))
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PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6))
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PRecall.append(TP/(len(one2nFile)+1e-6))
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NPrecise.append(TN/(TN+FN+1e-6))
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NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
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@ -202,23 +212,23 @@ def one2n_pr(evtDicts, pattern=1):
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ax.set_ylim([0, 1])
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ax.grid(True)
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ax.set_title('1:n Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}")
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ax.set_xlabel(f"Event Num: {len(one2nFile)}")
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ax.legend()
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plt.show()
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## ============================= 1:n 直方图'''
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fig, axes = plt.subplots(2, 2)
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axes[0, 0].hist(tpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0, 0].set_xlim([-0.2, 1])
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axes[0, 0].set_title('TP')
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axes[0, 0].set_title(f'TP: {len(tpsimi)}')
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axes[0, 1].hist(fpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0, 1].set_xlim([-0.2, 1])
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axes[0, 1].set_title('FP')
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axes[0, 1].set_title(f'FP: {len(fpsimi)}')
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axes[1, 0].hist(tnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1, 0].set_xlim([-0.2, 1])
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axes[1, 0].set_title('TN')
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axes[1, 0].set_title(f'TN: {len(tnsimi)}')
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axes[1, 1].hist(fnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1, 1].set_xlim([-0.2, 1])
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axes[1, 1].set_title('FN')
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axes[1, 1].set_title(f'FN: {len(fnsimi)}')
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plt.show()
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return fpevents
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@ -226,14 +236,14 @@ def one2n_pr(evtDicts, pattern=1):
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def main():
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'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
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init_eventdict(eventSourcePath, stype="source")
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init_eventdict(eventSourcePath, stype="data")
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'''2. 读取事件字典 '''
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evtDicts = read_eventdict(eventDataPath)
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'''3. 1:n 比对事件评估 '''
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fpevents = one2n_pr(evtDicts, pattern=3)
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fpevents = one2n_pr(evtDicts, pattern=1)
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fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
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with open(fpErrFile, "w") as file:
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@ -243,15 +253,16 @@ def main():
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if __name__ == '__main__':
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\ShoppingDict_pkfile"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\contrast"
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing"
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eventDataPath = os.path.join(resultPath, "evtobjs")
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similPath = os.path.join(resultPath, "simidata")
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if not os.path.exists(eventDataPath):
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os.makedirs(eventDataPath)
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if not os.path.exists(similPath):
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os.makedirs(similPath)
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# similPath = os.path.join(resultPath, "simidata")
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# if not os.path.exists(similPath):
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# os.makedirs(similPath)
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main()
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@ -626,12 +626,13 @@ if __name__ == '__main__':
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# eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"]
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stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
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stdBarcodePath = r"D:\全实时\source_data\bcdpath"
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stdFeaturePath = r"D:\全实时\source_data\stdfeats"
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stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
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stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\bcdpath"
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stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\stdfeats"
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eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\ShoppingDict_pkfile"]
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\contrast"
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eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"]
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\evtobjs"
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eventDataPath = os.path.join(resultPath, "evtobjs")
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similPath = os.path.join(resultPath, "simidata")
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if not os.path.exists(eventDataPath):
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@ -639,7 +640,7 @@ if __name__ == '__main__':
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if not os.path.exists(similPath):
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os.makedirs(similPath)
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# test_one2one()
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test_one2one()
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test_one2SN()
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@ -495,7 +495,7 @@ def contrast_pr(paths):
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if __name__ == "__main__":
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evtpaths = r"D:\全实时\source_data\2024122416"
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evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
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contrast_pr(evtpaths)
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@ -202,10 +202,10 @@ class ShoppingEvent:
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self.front_trackingfeats = frontdata[5]
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'''===========对应于 0/1_tracking_output.data ============================='''
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self.back_boxes = back_outdata[0]
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self.back_feats = back_outdata[1]
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self.front_boxes = front_outdata[0]
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self.front_feats = front_outdata[1]
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self.back_boxes = back_outdata
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self.back_feats = back_outdata
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self.front_boxes = front_outdata
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self.front_feats = front_outdata
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def from_datafile(self, eventpath):
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59
pipeline.py
59
pipeline.py
@ -103,14 +103,16 @@ def pipeline(
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# if pf_path.exists():
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# return
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'''====================== 构造 ShoppingDict 模块 ======================='''
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ShoppingDict = {"eventPath": eventpath,
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"eventName": evtname,
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"barcode": barcode,
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"eventType": '', # "input", "output", "other"
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"frontCamera": {},
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"backCamera": {},
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"one2n": []
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"one2n": [] #
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}
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@ -121,7 +123,7 @@ def pipeline(
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for vpath in vpaths:
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'''相机事件字典构造'''
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'''================= 1. 构造相机事件字典 ================='''
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CameraEvent = {"cameraType": '', # "front", "back"
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"videoPath": '',
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"imagePaths": [],
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@ -140,21 +142,21 @@ def pipeline(
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if bname.split('_')[0] == "1" or bname.find('front')>=0:
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CameraEvent["cameraType"] = "front"
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'''事件结果存储文件夹'''
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'''================= 2. 事件结果存储文件夹 ================='''
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if isinstance(vpath, list):
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savepath_pipeline_imgs = savepath_pipeline / Path("images")
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else:
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savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem))
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if not savepath_pipeline_imgs.exists():
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savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True)
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savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True)
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savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs")
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if not savepath_pipeline_subimgs.exists():
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savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True)
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'''Yolo + Resnet + Tracker'''
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'''================= 3. Yolo + Resnet + Tracker ================='''
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optdict["source"] = vpath
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optdict["save_dir"] = savepath_pipeline_imgs
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@ -162,11 +164,8 @@ def pipeline(
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CameraEvent["yoloResnetTracker"] = yrtOut
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# bboxes = np.empty((0, 9), dtype = np.float32)
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# for frameDict in yrtOut:
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# bboxes = np.concatenate([bboxes, frameDict["tboxes"]], axis=0)
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'''================= 4. tracking ================='''
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'''(1) 生成用于 tracking 模块的 boxes、feats'''
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trackerboxes = np.empty((0, 9), dtype=np.float64)
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trackefeats = {}
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for frameDict in yrtOut:
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@ -179,7 +178,7 @@ def pipeline(
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trackefeats.update({f"{fid}_{bid}": ffeats[f"{fid}_{bid}"]})
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'''tracking'''
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'''(2) tracking, 后摄'''
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if CameraEvent["cameraType"] == "back":
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vts = doBackTracks(trackerboxes, trackefeats)
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vts.classify()
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@ -187,7 +186,8 @@ def pipeline(
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CameraEvent["tracking"] = vts
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ShoppingDict["backCamera"] = CameraEvent
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'''(2) tracking, 前摄'''
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if CameraEvent["cameraType"] == "front":
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vts = doFrontTracks(trackerboxes, trackefeats)
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vts.classify()
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@ -196,11 +196,12 @@ def pipeline(
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CameraEvent["tracking"] = vts
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ShoppingDict["frontCamera"] = CameraEvent
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'''========================== 保存模块 ================================='''
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'''(1) 保存 ShoppingDict 事件'''
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with open(str(pf_path), 'wb') as f:
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pickle.dump(ShoppingDict, f)
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'''(2) 保存 Tracking 输出的运动轨迹子图，并记录相似度'''
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for CamerType, vts in event_tracks:
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if len(vts.tracks)==0: continue
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if CamerType == 'front':
|
||||
@ -215,16 +216,14 @@ def pipeline(
|
||||
imgdict.update(y["imgs"])
|
||||
featdict.update(y["feats"])
|
||||
simidict.update(y["featsimi"])
|
||||
|
||||
|
||||
|
||||
for track in vts.Residual:
|
||||
if isinstance(track, np.ndarray):
|
||||
save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype, featdict)
|
||||
else:
|
||||
save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_subimgs, ctype, featdict)
|
||||
|
||||
|
||||
'''轨迹显示模块'''
|
||||
'''(3) 轨迹显示与保存'''
|
||||
illus = [None, None]
|
||||
for CamerType, vts in event_tracks:
|
||||
if len(vts.tracks)==0: continue
|
||||
@ -279,15 +278,15 @@ def main():
|
||||
if item.is_dir():
|
||||
# item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
|
||||
parmDict["eventpath"] = item
|
||||
pipeline(**parmDict)
|
||||
# pipeline(**parmDict)
|
||||
|
||||
# try:
|
||||
# pipeline(**parmDict)
|
||||
# except Exception as e:
|
||||
# errEvents.append(str(item))
|
||||
k+=1
|
||||
if k==2:
|
||||
break
|
||||
try:
|
||||
pipeline(**parmDict)
|
||||
except Exception as e:
|
||||
errEvents.append(str(item))
|
||||
# k+=1
|
||||
# if k==100:
|
||||
# break
|
||||
|
||||
errfile = os.path.join(parmDict["savepath"], f'error_events.txt')
|
||||
with open(errfile, 'w', encoding='utf-8') as f:
|
||||
|
||||
@ -216,7 +216,7 @@ def yolo_resnet_tracker(
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# det = det.cpu().numpy()
|
||||
det = det.cpu().numpy()
|
||||
## ================================================================ writed by WQG
|
||||
'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
0 1 2 3 4 5 6 7 8
|
||||
|
||||
@ -2,7 +2,10 @@
|
||||
"""
|
||||
Created on Thu May 30 14:03:03 2024
|
||||
|
||||
现场测试性能分析
|
||||
轨迹分析现场测试性能分析:
|
||||
(1) 读取 data 文件中的轨迹数据，绘制轨迹图
|
||||
(2) 读取本地运行 Yolo+Rsenet+Tracker+Tracking 的数据，绘制轨迹图
|
||||
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user