manually merge the branch of evalution

.idea/.gitignore

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-# Default ignored files
-/shelf/
-/workspace.xml

.idea/ReIDMixLearning.iml

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-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.9 (python)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>

.idea/inspectionProfiles/Project_Default.xml

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-  <profile version="1.0">
-    <option name="myName" value="Project Default" />
-    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
-      <option name="ignoredPackages">
-        <value>
-          <list size="20">
-            <item index="0" class="java.lang.String" itemvalue="scipy" />
-            <item index="1" class="java.lang.String" itemvalue="six" />
-            <item index="2" class="java.lang.String" itemvalue="threadpoolctl" />
-            <item index="3" class="java.lang.String" itemvalue="Werkzeug" />
-            <item index="4" class="java.lang.String" itemvalue="sklearn" />
-            <item index="5" class="java.lang.String" itemvalue="wrapt" />
-            <item index="6" class="java.lang.String" itemvalue="gast" />
-            <item index="7" class="java.lang.String" itemvalue="MarkupSafe" />
-            <item index="8" class="java.lang.String" itemvalue="numpy" />
-            <item index="9" class="java.lang.String" itemvalue="tensorflow-io-gcs-filesystem" />
-            <item index="10" class="java.lang.String" itemvalue="gunicorn" />
-            <item index="11" class="java.lang.String" itemvalue="click" />
-            <item index="12" class="java.lang.String" itemvalue="Jinja2" />
-            <item index="13" class="java.lang.String" itemvalue="charset-normalizer" />
-            <item index="14" class="java.lang.String" itemvalue="keras" />
-            <item index="15" class="java.lang.String" itemvalue="tensorflow-cpu" />
-            <item index="16" class="java.lang.String" itemvalue="itsdangerous" />
-            <item index="17" class="java.lang.String" itemvalue="tf-estimator-nightly" />
-            <item index="18" class="java.lang.String" itemvalue="Flask" />
-            <item index="19" class="java.lang.String" itemvalue="google-auth" />
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-      </option>
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-  </profile>
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.idea/inspectionProfiles/profiles_settings.xml

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-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

.idea/misc.xml

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-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="MarkdownSettingsMigration">
-    <option name="stateVersion" value="1" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (python)" project-jdk-type="Python SDK" />
-</project>

.idea/modules.xml

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-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/ReIDMixLearning.iml" filepath="$PROJECT_DIR$/.idea/ReIDMixLearning.iml" />
-    </modules>
-  </component>
-</project>

evaluate_CMC.py

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+import matplotlib.pyplot as plt
+import torch
+from torchvision import transforms
+import numpy as np
+import cv2
+import os
+
+from model import my_model
+import param
+
+
+def extractidfeature(id_path: str, extractor, p_device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
+    img_id = cv2.imread(id_path)
+    raw_transformer = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((128,64)),# hxw
+    transforms.ToTensor(),
+    ])
+    tensor_id = extractor(raw_transformer(img_id).unsqueeze(dim=0).to(p_device))
+    
+    return tensor_id.detach().cpu().numpy()
+
+if __name__ == '__main__':
+    my_option = param.Parameters()                
+      
+    extractor = my_model.RestNet18()    # load model in head
+    extractor.load_state_dict(torch.load(my_option.weights_reid))
+    extractor = extractor.to(my_option.device)
+    
+    # gallery_index = 3
+    gallery_path = f"./mydataset/gallery/"
+    classes = os.listdir(gallery_path)
+    cmc_sum = []
+    cmc_final = []
+    for query_index in range(1, 6+1):                #query是从1开始的
+
+        query_path = f"./mydataset/query/cam{query_index}/"   # Query path
+                #Gallary path，这里直接对gallary文件夹下所有图片进行分析
+        print("--------------query cam is ", query_index)
+        for cls in classes:
+            if cls != f"{query_index}":
+                print("——————————gallery cam is", cls)
+                gallery_path = f"./mydataset/gallery/{cls}/"
+                query_feature_list = [] # 计算每个query的特征向量保存在列表中
+                acck_l_list = []        # 二维列表（列表的列表），其元素（也是列表）存储单个query的"acck"("cmc")
+                cmc_k_list = []         # 存储最终的CMC数字（所有query取平均）
+                query_list = os.listdir(query_path)     # 为每张图片生成路径并保存在列表变量中
+                query_list.sort()
+                gallery_list = os.listdir(gallery_path) # 为每张图片生成路径并保存在列表变量中
+                with torch.no_grad():
+                    # 对于每个query，（这句话加在本循环内每个注释）
+                    for path in query_list:
+                        distance_dict = {}
+                        parsed_query_id = path.split("_")[1]
+                        parsed_query_id = parsed_query_id.split(".")[0]
+                        parsed_query_id = int(parsed_query_id)
+                        if parsed_query_id == 4 and cls == "5":
+                            continue
+                        print(parsed_query_id)
+                        id_feature = extractidfeature(query_path+path, extractor)
+                        query_feature_list.append(id_feature)
+                        counter = 0
+                        # 计算每个gallary图片到目标query的特征距离
+                        for p in gallery_list:
+                                if counter%10 == 9:     # 抽样，相当于设置帧间隔
+                                    img_path = gallery_path+p
+                                    gallery_feature = extractidfeature(img_path, extractor)
+                                    distance_dict[p] = np.sum(np.abs(id_feature-gallery_feature))
+                                counter += 1
+                        matched = sorted(distance_dict.items(), key=lambda x : x[1])    # 按特征距离排序
+                        # 储存k从前1到前10名的是否（存在命中的ID）
+                        temp_acck = [int(matched[0][0].split("_")[2].split(".")[0])==parsed_query_id]     # ID相同为True，否则False
+                        # 获取前k个acck情况
+                        for i in range(1,11):
+                            parsed_gallery_id = matched[i][0].split("_")[2].split(".")[0]
+                            parsed_gallery_id = int(parsed_gallery_id)           # 解析出gallary的ID
+                            print(parsed_gallery_id)  # 解析出gallary的ID
+                            temp_acck.append(parsed_gallery_id==parsed_query_id or temp_acck[i-1])    # or用意是表明如果Acc(k-1)已经为True，那么Acck必然为True（递推）
+                        print(temp_acck)
+                        acck_l_list.append(temp_acck)
+                    # 计算不同k下cmc的具体数值
+                    for k in range(1, 11):
+                        acc_counter = 0.0
+                        length = len(acck_l_list)
+                        for results in acck_l_list:
+                            acc_counter += results[k]
+                        cmc_k_list.append(acc_counter/length)
+                    #打印以供检查
+                    print(cmc_k_list)
+                    cmc_sum.append(cmc_k_list)
+                    k_index = [k for k in range(1, 11)]
+                    #plot figure并保存（不会显示）
+                    plt.clf()
+                    plt.plot(k_index, cmc_k_list)
+                    plt.xlabel("k")
+                    plt.ylabel("ACCK")
+                    plt.ylim(0.0,1.05)
+                    plt.title(f"CMC:query_cam{query_index} and gallery {cls}")
+                    for x, y in zip(k_index, cmc_k_list):
+                        plt.text(x, y + 0.02, str(round(y, 3)), ha='center', va='bottom', fontsize=10.5)
+                    plt.draw()
+                    plt.savefig(f"./CMC/query_cam{query_index}_and_gallery_{cls}.jpg")
+    for k in range(10):
+        acc_counter = 0.0
+        length = len(cmc_sum)
+        for results in cmc_sum:
+            acc_counter += results[k]
+        cmc_final.append(acc_counter / length)
+    print(cmc_final)
+    k_index = [k for k in range(1, 11)]
+    # plot figure并保存（不会显示）
+    plt.clf()
+    plt.plot(k_index, cmc_final)
+    plt.xlabel("k")
+    plt.ylabel("ACCK")
+    plt.ylim(0.0, 1.05)
+    plt.title(f"CMC:query and gallery")
+    for x, y in zip(k_index, cmc_final):
+        plt.text(x, y + 0.02, str(round(y,3)), ha='center', va='bottom', fontsize=10.5)
+    plt.draw()
+    plt.savefig(f"./CMC/query_gallery.jpg")

evaluate_mAP.py

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+#####
+# 保存query和前十个配准的gallery图像，并计算mAP(mean average precision)
+######
+import shutil
+import matplotlib.pyplot as plt
+import torch
+from torchvision import transforms
+import numpy as np
+import cv2
+import os
+
+from model import my_model
+import param
+
+
+def extractidfeature(id_path: str, extractor, p_device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
+    img_id = cv2.imread(id_path)
+    raw_transformer = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((128,64)),# hxw
+    transforms.ToTensor(),
+    ])
+    tensor_id = extractor(raw_transformer(img_id).unsqueeze(dim=0).to(p_device))
+    
+    return tensor_id.detach().cpu().numpy()
+
+
+if __name__ == '__main__':
+    my_option = param.Parameters()
+    dislist = {}
+    
+    extractor = my_model.RestNet18()    # load model in head
+    extractor.load_state_dict(torch.load(my_option.weights_reid))
+    extractor = extractor.to(my_option.device)
+    
+    gallery_index = 4
+    camera_index = 1 # 第几个摄像头
+    query_path = f"./mydataset/query/cam{camera_index}/"
+    galla_path = f"./mydataset/gallery/{gallery_index}/"
+
+    gallary_list = os.listdir(galla_path)
+    count = 0
+    number_query = 12
+
+#####计算mAP使用的变量
+    mAP = 0
+    precision = 0
+    sum_precision = 0
+    AP = 0
+    sum_AP = 0
+    number_true_gallery = 0
+#######
+
+    with torch.no_grad():
+        for i in range(1, 1+number_query):
+            count = 0
+            dislist.clear()
+            id_try = extractidfeature(query_path+f"/{camera_index}_{i}.jpg", extractor)
+            for gal in gallary_list:
+                count += 1
+                if count % 5 == 0:
+                    id_tar = extractidfeature(galla_path+'/'+gal, extractor)
+                    dist = np.sum(np.abs(id_try-id_tar))
+                    dislist[gal] = float(dist)
+                # if count > 10000:
+                #     break
+            matched = sorted(dislist.items(), key=lambda x : x[1])
+            print(matched)
+            try:
+                os.mkdir("match/" + f"{camera_index}_{i}")
+            except:
+                pass
+
+            img = plt.imread(query_path+f"/{camera_index}_{i}.jpg")
+            plt.subplot(1, 11, 1)
+            plt.title(f"q_{i}_g_{gallery_index}")
+            plt.imshow(img)
+            plt.xticks([])
+            plt.yticks([])
+            ranking = 1
+            ranking_true_gallery = 1
+            for path in matched[0:10]:
+                s_path = galla_path+'/'+path[0]
+                t_path = "match/" + f"{camera_index}_{i}" + "/" + path[0]
+                # print(s_path, t_path)
+                shutil.copy(s_path, t_path)
+                ID = path[0].split('_')
+                # print(ID)
+                ID = int(ID[2].split(".")[0])
+                print(i, ID)
+                img = plt.imread(s_path)
+                plt.subplot(1, 11, ranking+1)
+                if ID == i:
+                    plt.title(ranking, color='black')
+                    precision = ranking_true_gallery/ranking
+                    sum_precision += precision
+                    ranking_true_gallery += 1
+
+                else:
+                    plt.title(ranking, color='red')
+                plt.imshow(img)
+                plt.xticks([])
+                plt.yticks([])
+                ranking += 1
+            # plt.show()
+            plt.draw()
+            plt.savefig(f"query{i}_cam{camera_index}_and_gallery_{gallery_index}.jpg")
+            number_true_gallery = ranking_true_gallery-1
+            if 0 != number_true_gallery:
+                AP = sum_precision / number_true_gallery
+            else:
+                AP = 0
+            sum_precision = 0
+            sum_AP += AP
+            print("query_", i, "AP==", AP)
+        mAP = sum_AP/number_query
+        print("mAP == ", mAP)
+

evaluate_rank10.py

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+import shutil
+import matplotlib.pyplot as plt
+import torch
+from torchvision import transforms
+import numpy as np
+import cv2
+import os
+
+from model import my_model
+import param
+
+
+
+def extractidfeature(id_path: str, extractor, p_device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
+    img_id = cv2.imread(id_path)
+    raw_transformer = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((128,64)),# hxw
+    transforms.ToTensor(),
+    ])
+    tensor_id = extractor(raw_transformer(img_id).unsqueeze(dim=0).to(p_device))
+    
+    return tensor_id.detach().cpu().numpy()
+
+# example        
+if __name__ == '__main__':
+    dislist = {}
+    extractor_model_path = "./deep_sort/deep/checkpoint/market_bot_R50-ibn.pth"
+    cfg_path = "./fastreid/cfgs/Market1501/bagtricks_R50-ibn.yml"
+    query_path = "D:\\study\\python\\1zhouxue\\UESTC_ReID_Dataset_V3\\query\\cam6"
+    galla_path = "D:\\study\\python\\1zhouxue\\UESTC_ReID_Dataset_V3\\gallery"
+    camera_index = 6 # 第几个摄像头
+    gallary_list = os.listdir(galla_path)
+    count = 0
+    
+    my_option = param.Parameters()
+    
+    extractor = my_model.RestNet18()    # load model in head
+    extractor.load_state_dict(torch.load(my_option.weights_reid))
+    extractor = extractor.to(my_option.device)
+    
+    with torch.no_grad():
+        for i in range(1, 7):   
+            count = 0
+            dislist.clear()
+            id_try = extractidfeature(query_path+f"/{camera_index}_{i}.jpg", extractor)
+            for gal in gallary_list:
+                count += 1
+                if count % 5 == 0:
+                    id_tar = extractidfeature(galla_path+'/'+gal, extractor)
+                    dist = np.sum(np.abs(id_try-id_tar))
+                    dislist[gal] = float(dist)
+                if count > 10000:
+                    break
+            matched = sorted(dislist.items(), key=lambda x : x[1])
+            print(matched)
+            try:
+                os.mkdir("match/" + f"{camera_index}_{i}")
+            except:
+                pass
+
+            img = plt.imread(query_path+f"/{camera_index}_{i}.jpg")
+            plt.subplot(1, 11, 1)
+            plt.title(f"query_{i}")
+            plt.imshow(img)
+            plt.xticks([])
+            plt.yticks([])
+            x = 1
+            for path in matched[0:10]:
+                s_path = galla_path+'/'+path[0]
+                t_path = "match/" + f"{camera_index}_{i}" + "/" + path[0]
+                print(s_path, t_path)
+                shutil.copy(s_path, t_path)
+
+                ID = path[0].split('_')
+                # print(ID)
+                ID = int(ID[0])
+                img = plt.imread(s_path)
+                plt.subplot(1, 11, x+1)
+                if ID == i:
+                    plt.title(x)
+                else:
+                    plt.title(x, color='red')
+                plt.imshow(img)
+                plt.xticks([])
+                plt.yticks([])
+                x += 1
+            plt.show()
+            
+            

param.py

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+import torch
+
+class Parameters(object):
+    def __init__(self) -> None:
+        self.weights_yolo = "./best.pt"
+        self.weights_reid = "./resnet18.pth"
+        self.conf_thres   = 0.35
+        self.iou_thres    = 0.70
+        self.device       = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.nosave       = False
+        self.classes      = [0]
+        self.agnostic_nms = None
+        self.augment      = None
+        self.query_index  = 1
+        self.gallary_index= 2
+        pass

train.py

@@ -46,6 +46,10 @@ if __name__ == '__main__':
     raw_transformer = transforms.Compose([
     transforms.ToPILImage(),
     transforms.Resize((128,64)),# hxw
+    transforms.RandomResizedCrop((100,50)),
+    transforms.RandomAutocontrast(),
+    transforms.RandomHorizontalFlip(),
+    transforms.Resize((128,64)),# hxw
     transforms.ToTensor(),
     ])