update for bakeup

tracking/contrast_analysis.py

@@ -10,6 +10,43 @@ sys.path.append(r"D:\DetectTracking")
 from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
 from tracking.utils.plotting import draw_tracking_boxes
 
+VideoFormat = ['.mp4', '.avi']
+def video2imgs(videopath, savepath):
+    k = 0
+    have = False
+    for filename in os.listdir(videopath):
+        file, ext = os.path.splitext(filename)
+        if ext not in VideoFormat:
+            continue
+        
+        basename = os.path.basename(videopath)
+        imgbase = basename + '_' + file
+        imgdir = os.path.join(savepath, imgbase)
+        if not os.path.exists(imgdir):
+            os.mkdir(imgdir)
+
+        video = os.path.join(videopath, filename)
+        cap = cv2.VideoCapture(video)
+        i = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            imgp = os.path.join(imgdir, file+f"_{i}.png")
+            i += 1
+            cv2.imwrite(imgp, frame)
+        cap.release()
+        
+        print(filename + f" haved resolved")
+        
+        k+=1
+        if k==1000:
+            break
+
+
+
+
+
 def showHist(err, correct):
     err = np.array(err)
     correct = np.array(correct)
@@ -22,24 +59,28 @@ def showHist(err, correct):
     axs[1].hist(correct, bins=50, edgecolor='black')
     axs[1].set_xlim([0, 1])
     axs[1].set_title('correct')
-    plt.show()
+    # plt.show()
+    
+    return plt
 
-def showgrid(recall, prec, ths):
+def show_recall_prec(recall, prec, ths):
     # x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist()
     fig = plt.figure(figsize=(10, 6))
     plt.plot(ths, recall, color='red', label='recall')
     plt.plot(ths, prec, color='blue', label='PrecisePos')
     plt.legend()
-    plt.xlabel('threshold')
+    plt.xlabel(f'threshold')
     # plt.ylabel('Similarity')
     plt.grid(True, linestyle='--', alpha=0.5)
-    plt.savefig('accuracy_recall_grid.png')
-    plt.show()
+    # plt.savefig('accuracy_recall_grid.png')
+    # plt.show()
     # plt.close()
+    
+    return plt
 
 
 def compute_recall_precision(err_similarity, correct_similarity):
-    ths = np.linspace(0, 1, 11)
+    ths = np.linspace(0, 1, 51)
     recall, prec = [], []
     for th in ths:
         TP = len([num for num in correct_similarity if num >= th])
@@ -50,9 +91,7 @@ def compute_recall_precision(err_similarity, correct_similarity):
         else:
             prec.append(TP / (TP + FP))
             recall.append(TP / (len(err_similarity) + len(correct_similarity)))
-            
-    showgrid(recall, prec, ths)
-    return recall, prec
+    return recall, prec, ths
 
 
 # =============================================================================
@@ -291,7 +330,8 @@ def performance_evaluate(all_list, isshow=False):
     
     '''3. 计算比对性能 '''
     if isshow:
-        compute_recall_precision(err_similarity, correct_similarity)
+        recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+        show_recall_prec(recall, prec, ths)
         showHist(err_similarity, correct_similarity)
         
     return errpairs, corrpairs, err_similarity, correct_similarity
@@ -306,8 +346,7 @@ def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
 
     '''1. 读取 deletedBarcode 文件 '''
     all_list = read_deletedBarcode_file(del_barcode_file)
-    
-    
+
     '''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
     errpairs, corrpairs, _, _ = performance_evaluate(all_list)
 
@@ -319,12 +358,62 @@ def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
     
         if saveimgs:
             save_tracking_imgpairs(errpair, basepath, savepath)
-    
-    
 
-    
     return relative_paths
+
+
+def contrast_loop(fpath):
+    savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
+    # savepath = r'D:\contrast\dataset\1_to_n\illustration'
+    if not os.path.exists(savepath):
+        os.mkdir(savepath)
+        
+    if os.path.isfile(fpath):
+        fpath, filename = os.path.split(fpath)
     
+    
+        
+    
+    
+    
+    BarLists, blists = {}, []
+    for filename in os.listdir(fpath):
+        file = os.path.splitext(filename)[0][15:]
+
+        filepath = os.path.join(fpath, filename)
+        blist = read_deletedBarcode_file(filepath)
+ 
+        BarLists.update({file: blist})
+        blists.extend(blist)
+        
+        
+        
+    BarLists.update({file: blist})
+    BarLists.update({"Total": blists})
+    for file, blist in BarLists.items():        
+        errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist)
+        
+        recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+        
+        plt1 = show_recall_prec(recall, prec, ths)
+        # plt1.show()
+        plt.xlabel(f'threshold, Num: {len(blist)}')
+        plt1.savefig(os.path.join(savepath, file+'_pr.png'))
+        # plt1.close()
+        
+        # plt2 = showHist(err_similarity, correct_similarity)
+        # plt2.show()
+        # plt2.savefig(os.path.join(savepath, file+'_hist.png'))
+        # plt.close()
+
+        
+        
+def main1():
+    fpath = r'D:\contrast\dataset\1_to_n\0719'
+    fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\good'
+    
+    contrast_loop(fpath)
+
 def main():
     del_barcode_file = 'D:/contrast/dataset/compairsonResult/deletedBarcode_20240709_pm.txt'
     basepath = r'D:\contrast\dataset\1_to_n\709'
@@ -335,13 +424,18 @@ def main():
     except Exception as e:
         print(f'Error Type: {e}')
     
-
-
+def resolve_vidoes():
+    videopath = r"\\192.168.1.28\share\测试_202406\0719\719_1\20240719-103533_"
+    savepath = r"D:\contrast\result"
     
-
+    video2imgs(videopath, savepath)
 
 if __name__ == '__main__':
-    main()
+    # main()
+    main1()
+    
+    # resolve_vidoes()
+