Panoramic

DetectFineSortVideo.py

@@ -57,10 +57,12 @@ class DetFineVid():
                 if frame.shape[1] == 3840:
                     gp.fontScale = 3
                 elif frame.shape[1] == 2560:
-                    gp.fontScale = 2
+                    gp.fontScale = 1
                 else:
                     gp.fontScale = 1
-
+        for i in range(20):
+            res, frame = cap.read()
+            print("skip{}".format(i))
 
         num_image = 0
         while True:
@@ -129,6 +131,7 @@ class DetFineVid():
 
 def commanddeal():
     SavePath = "/data1/Hatcher/model/project/data_MPKFNet/result/"
+    print("starting ",gp.VideoPath)
     DFS = DetFineVid(gp.VideoPath,SavePath)
     DFS.StartRec()
     DFS.conVideo()
@@ -261,8 +264,8 @@ def globalInit():  #deal all file in a folder
 if __name__ == '__main__':
     multiprocessing.set_start_method("spawn")
     rootPath = "/data/DataManagement/Project/005_kunmingdianchi/0002_20220412_800_200/研究院-鱼视频/"
-    filedeal(rootPath)
-    #commanddeal()
+    #filedeal(rootPath)
+    commanddeal()
 
 
 

System_I.py

@@ -1429,7 +1429,7 @@ class RealDeal():
             tprint4 = time.time()
             if gp.DebugPrint_flag:
                 print("tprint3 is ", (tprint4 - tprint3) * 1000)
-            draw = plot_one_box(xyxy, draw, height_or,width_or,label=label, Index=index,color=colors, line_thickness=int(3*gp.radioShowtext))
+            draw = plot_one_box(xyxy, draw, height_or,width_or,label=label, Index=index,color=colors, line_thickness=int(1.5*gp.radioShowtext))
         tpChin1 = time.time()
         if num_image > gp.showThresh :
             gp.num_count_old = len(boxes_fine)

common.py

@@ -8,9 +8,9 @@ import  os,torch, threading,argparse
 class global_para():
     #check()
     parser1 = argparse.ArgumentParser(description="CUI")
-    parser1.add_argument("--video_num", default='/data1/Hatcher/model/project/data_MPKFNet/rawdata/PYH-DEMO.mp4', type=str,
+    parser1.add_argument("video_num", default='/data1/Hatcher/model/project/data_MPKFNet/rawdata/竹林嘴-3月3日候鸟视频.mp4', type=str,
                         help='Video name.')
-    parser1.add_argument("--GPU", default='6', type=str,
+    parser1.add_argument("GPU", default='2', type=str,
                         help='GPU.')
     parser1.add_argument("--thresh", default=0.4, type=float,
                         help='GPU.')
@@ -79,13 +79,13 @@ class global_para():
     Fine_thresh = 0.4
 
     # DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/exp21/weights/best.pt"
-    # FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-PYH-3-20.7/_42522_PYH-3/best.pth"
+    # FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-JXPYH-Bird-3-4-20220429/_42911_PYH-3/best.pth"
     DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/KunMingCuiHu-Fish2/weights/best.pt"
-    FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-DianChiFish-3-3-0.7/_4289_DianChiFish-4/best.pth"
+    FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-KMDC-Fish-5-1-20220505/_5510_DianChiFish-4/best.pth"
     # DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/BeijingCuiHu-Bird26/weights/best.pt"
     # FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-cui90_1model0.7/_42111_CUI-94-1/best.pth"
-    # BHG_chineseName = ["kong", '白鹤', '白头鹤','白枕鹤']#["kong","鲫鱼","麦穗鱼","鳑鲏"]
-    BHG_chineseName = ["kong","鲫鱼","麦穗鱼","鳑鲏"]
+    #BHG_chineseName = ["kong", '白鹤', '白头鹤','白枕鹤']
+    BHG_chineseName = ["kong","鲫鱼","麦穗鱼","鳑鲏","鲈鱼"]
 
     # BHG_chineseName = ['Kong', '白额雁', '白骨顶', '苍鹭', '苍鹭', '白秋沙鸭', '白腰草鹬', '斑头雁', '斑嘴鸭', '苍鹭', '赤膀鸭', '赤颈鸭', '苍鹭', '大麻鳽',
     #                    '豆雁', '反嘴鹬', '凤头鸊鷉', '凤头麦鸡', '凤头潜鸭', '黑鹳', '黑水鸡', '红嘴鸥', '鸿雁', '灰鹤', '灰雁', '绿翅鸭', '绿头鸭', '琵嘴鸭',
@@ -96,12 +96,13 @@ class global_para():
     #                    '白鹈鹕', '红头潜鸭', '苍鹰', '蒙古银鸥', '西方秧鸡', '赤嘴潜鸭', '阔嘴鹬', '雕鸮', '鹌鹑', '鹗', '黄苇鳽', '黑颈鹤', '小䴙䴘', '赤麻鸭']
     # BHG_chineseName =["kong","绿头鸭","斑嘴鸭","灰雁","鸿雁","kong","小鷿鷈"]
     spiec_number_old = [0 for i in range(len(BHG_chineseName))]
-    FineClassNum = 4
+    FineClassNum = 5
     color_show = (255, 255, 255)
     color_box = (255, 0, 0)
-    NoneLabel = "鱼"  # "鹤类"
+    NoneLabel = "鱼"#"鹤类" # "鱼"
     fine_thresh1 = 6000
-    fine_thresh2 = 10000
+    fine_thresh2 = 6000
+    ShowAllNumFlag = True
     fontScale = 1
 """
 1.change weight
@@ -113,6 +114,7 @@ class global_para():
 7. change main
 8. change fine_thresh2
 9. GPU
+10. change ShowAllNumFlag
 fontScale  plot file para to adjust chinese   400W ---2  
                                               200W ---1  
                                               800W ---3

get_fine_dataset.py

@@ -2,15 +2,12 @@ import xml.dom.minidom
 from tqdm import tqdm
 from glob import glob
 import cv2
-import os
-from PIL import Image
+import os,argparse
 import numpy as np
 from jpg import jpg
-txt_root_path = "/data/DataManagement/Project/005_kunmingdianchi/0002_20220412_800_200/Dataset/VOC0050201"
 #xml_files = glob('./temp2/*.xml')
-image_T = 50
-num_count = 0
-root_path = "/data/DataManagement/Project/005_kunmingdianchi/0002_20220412_800_200/Dataset/VOC0050201/Annotations/"
+
+class_name = ["Anser albifrons" ,"Fulica atra" ,"Leucogeranus leucogeranus" ,"Egretta garzetta","Platalea leucorodia" ,"Mergellus albellus" ,"Grus monacha" ,"Tringa ochropus" ,"Numenius arquata" ,"Anser indicus","Anas zonorhyncha","Ardea cinerea","Mareca strepera" ,"Mareca penelope" ,"Tadorna ferruginea","Ardea alba","Numenius madagascariensis" ,"Botaurus stellaris"  ,"Ciconia boyciana"  ,"Anser fabalis" ,"Recurvirostra avosetta" ,"Podiceps cristatus","Vanellus vanellus" ,"Aythya fuligula" ,"Tringa erythropus","Calidris alpina" ,"Ciconia nigra" ,"Gallinula chloropus","Porphyrio poliocephalus","Larus ridibundus"  ,"Anser cygnoides"  ,"Charadrius alexandrinusw" ,"Pluvialis squatarola" ,"Grus grus" ,"Anser anser" ,"Pluvialis fulva" ,"Charadrius dubius"  ,"Anas crecca","Anas platyrhynchos","Spatula clypeata","Phalacrocorax carbo","Mergus merganser" ,"Tadorna tadorna"  ,"Tringa nebulariaw","Bucephala clangula" ,"Gallinago gallinago" ,"Anser erythropus","Cygnus columbianus" ,"Ichthyaetus ichthyaetus","Anas acuta" ,"Ardea intermedia"]
 def cv2_crop(im, box):
     '''cv2实现类似PIL的裁剪
 
@@ -32,111 +29,64 @@ def convert(size, box):
     h = h*dh
     return (x,y,w,h)
 
-# for xml_path in os.listdir(root_path):
-#     xml_files = glob(os.path.join(root_path,xml_path,"*.xml"))
-#     print(os.path.join(root_path.replace("xml","image"),xml_path))
-#     jpg(os.path.join(root_path.replace("xml","image"),xml_path))
-#     f = open("./Hatcher_ALL.txt", "a")
-#     txt_lists = []
-#     for xml_file in tqdm(xml_files):
-#
-#         print(xml_file+" is doing")
-#         DOMTree = xml.dom.minidom.parse(xml_file)
-#         collection = DOMTree.documentElement
-#         #img_name = collection.getElementsByTagName("filename")[0].childNodes[0].data
-#         img_name = xml_file.replace("\\xml\\","\\image\\").replace(".xml",".jpg")
-#         objects = collection.getElementsByTagName("object")
-#         #img = cv2.imread(img_name)
-#         img = cv2.imdecode(np.fromfile(img_name,dtype=np.uint8),-1)
-#         #cv2.imwrite(img_name,img)
-#         #img = cv2.imdecode(np.fromfile(img_name,dtype=np.uint8),-1)
-#         # im = Image.open(img_name)
-#         # im.save(img_name, quality=100)
-#         # im = Image.open(img_name)
-#         for object_id in objects:
-#             xmin = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('xmin')[0].childNodes[0].data
-#             ymin = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('ymin')[0].childNodes[0].data
-#             xmax = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('xmax')[0].childNodes[0].data
-#             ymax = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('ymax')[0].childNodes[0].data
-#             name = object_id.getElementsByTagName('name')[0].childNodes[0].data
-#             xmin = int(xmin)
-#             xmax = int(xmax)
-#             ymin = int(ymin)
-#             ymax = int(ymax)
-#             class_index = class_name.index(str(name).replace("\t","").strip())
-#             img1 = cv2_crop(img,(xmin, ymin,xmax, ymax))
-#             cv2.imwrite("H:\\Fine-grained-identification\\2021.3.25\Hatcher_One\\" + str(name).replace("\t","").strip().replace(" ","_" )+"_{}.jpg".format(num_count), img1)
-#             txt_lists.append(str(name).replace("\t","").strip().replace(" ","_" )+"_{}.jpg".format(num_count)+"  "+str(class_index)+'\n')
-#             #f.write(str(name).replace("\t","").strip().replace(" ","_" )+"_{}.jpg".format(num_count)+"  "+str(class_index)+'\n')
-#             num_count += 1
-#     for txt_list in txt_lists:
-#         f.write(txt_list)
-#     f.close()
-#     f_num = open("./num.txt", "w")
-#     f_num.write(str(num_count))
-#     f_num.close()
-#     print(num_count)
-#     for xml_file in tqdm(xml_files):
-#         os.remove(xml_file)
-#
-#         #os.remove(xml_file)
-# print(num_count)
+def VocToFineWithLabel(num_count,root_path,image_T):
+    xml_files = glob(os.path.join(root_path,"*.xml"))
+    jpg(os.path.join(root_path.replace("Annotations","JPEGImages")))
+    txt_lists = []
+    for xml_file in tqdm(xml_files):
 
-xml_files = glob(os.path.join(root_path,"*.xml"))
-#print(os.path.join(root_path.replace("Annotations","JPEGImages")))
-jpg(os.path.join(root_path.replace("Annotations","JPEGImages")))
-f = open(txt_root_path+"/Hatcher_ALL.txt", "a")
-txt_lists = []
-for xml_file in tqdm(xml_files):
+        #print(xml_file+" is doing")
+        DOMTree = xml.dom.minidom.parse(xml_file)
+        collection = DOMTree.documentElement
+        #img_name = collection.getElementsByTagName("filename")[0].childNodes[0].data
+        img_name = xml_file.replace("Annotations","JPEGImages").replace(".xml",".jpg")
+        objects = collection.getElementsByTagName("object")
+        #img = cv2.imread(img_name)
+        img = cv2.imdecode(np.fromfile(img_name,dtype=np.uint8),-1)
+        #cv2.imwrite(img_name,img)
+        #img = cv2.imdecode(np.fromfile(img_name,dtype=np.uint8),-1)
+        # im = Image.open(img_name)
+        # im.save(img_name, quality=100)
+        # im = Image.open(img_name)
+        for object_id in objects:
+            xmin = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('xmin')[0].childNodes[0].data
+            ymin = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('ymin')[0].childNodes[0].data
+            xmax = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('xmax')[0].childNodes[0].data
+            ymax = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('ymax')[0].childNodes[0].data
+            name = object_id.getElementsByTagName('name')[0].childNodes[0].data
+            ratio_x = (3840.0/3840.0)
+            ratio_y = (2160.0/2160.0)
+            #print(ratio_x,"    ",ratio_y)
+            #print(type(xmin))
+            #exit()
+            xmin = int(float(xmin)*ratio_x)
+            xmax = int(float(xmax)*ratio_x)
+            ymin = int(float(ymin)*ratio_y)
+            ymax = int(float(ymax)*ratio_y)
+            if ((xmax - xmin)<image_T) | ((ymax - ymin)<image_T):
+                continue
+            img1 = cv2_crop(img,(xmin, ymin,xmax, ymax))
+            if not os.path.exists(os.path.join(root_path.replace("Annotations","FineDataset_{}".format(str(image_T))),str(name).replace("\t","").strip())):
+                os.makedirs(os.path.join(root_path.replace("Annotations","FineDataset_{}".format(str(image_T))),str(name).replace("\t","").strip()))
+            cv2.imwrite(os.path.join(root_path.replace("Annotations","FineDataset_{}".format(str(image_T))),str(name).replace("\t","").strip(),"{}.jpg".format(str(num_count).zfill(5))), img1)
+            #txt_lists.append(str(name).replace("\t","").strip()+"{}.jpg".format(str(num_count).zfill(5))+"  "+str(class_index)+'\n')
+            #f.write(str(name).replace("\t","").strip().replace(" ","_" )+"_{}.jpg".format(num_count)+"  "+str(class_index)+'\n')
+            num_count += 1
+    print(num_count)
+    # for xml_file in tqdm(xml_files):
+    #     os.remove(xml_file)
 
-    #print(xml_file+" is doing")
-    DOMTree = xml.dom.minidom.parse(xml_file)
-    collection = DOMTree.documentElement
-    #img_name = collection.getElementsByTagName("filename")[0].childNodes[0].data
-    img_name = xml_file.replace("Annotations","JPEGImages").replace(".xml",".jpg")
-    objects = collection.getElementsByTagName("object")
-    #img = cv2.imread(img_name)
-    img = cv2.imdecode(np.fromfile(img_name,dtype=np.uint8),-1)
-    #cv2.imwrite(img_name,img)
-    #img = cv2.imdecode(np.fromfile(img_name,dtype=np.uint8),-1)
-    # im = Image.open(img_name)
-    # im.save(img_name, quality=100)
-    # im = Image.open(img_name)
-    for object_id in objects:
-        xmin = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('xmin')[0].childNodes[0].data
-        ymin = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('ymin')[0].childNodes[0].data
-        xmax = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('xmax')[0].childNodes[0].data
-        ymax = object_id.getElementsByTagName('bndbox')[0].getElementsByTagName('ymax')[0].childNodes[0].data
-        name = object_id.getElementsByTagName('name')[0].childNodes[0].data
-        ratio_x = (3840.0/3840.0)
-        ratio_y = (2160.0/2160.0)
-        #print(ratio_x,"    ",ratio_y)
-        #print(type(xmin))
-        #exit()
-        xmin = int(float(xmin)*ratio_x)
-        xmax = int(float(xmax)*ratio_x)
-        ymin = int(float(ymin)*ratio_y)
-        ymax = int(float(ymax)*ratio_y)
-        if ((xmax - xmin)<image_T) | ((ymax - ymin)<image_T):
-            continue
-        #class_index = class_name.index(str(name).replace("\t","").strip())
-        img1 = cv2_crop(img,(xmin, ymin,xmax, ymax))
-        if not os.path.exists(os.path.join(root_path.replace("Annotations","FineDataset_{}".format(str(image_T))),str(name).replace("\t","").strip())):
-            os.makedirs(os.path.join(root_path.replace("Annotations","FineDataset_{}".format(str(image_T))),str(name).replace("\t","").strip()))
-        cv2.imwrite(os.path.join(root_path.replace("Annotations","FineDataset_{}".format(str(image_T))),str(name).replace("\t","").strip(),"{}.jpg".format(str(num_count).zfill(5))), img1)
-        #txt_lists.append(str(name).replace("\t","").strip()+"{}.jpg".format(str(num_count).zfill(5))+"  "+str(class_index)+'\n')
-        #f.write(str(name).replace("\t","").strip().replace(" ","_" )+"_{}.jpg".format(num_count)+"  "+str(class_index)+'\n')
-        num_count += 1
-for txt_list in txt_lists:
-    f.write(txt_list)
-f.close()
-f_num = open("./num.txt", "w")
-f_num.write(str(num_count))
-f_num.close()
-print(num_count)
-# for xml_file in tqdm(xml_files):
-#     os.remove(xml_file)
+            #os.remove(xml_file)
+    print(num_count)
+if __name__ == '__main__':
+    print("Starting dealing......")
+    parser1 = argparse.ArgumentParser(description="GYD")
+    parser1.add_argument("image_T", default='0001', type=str,
+                        help='Video name.')
+    parser1.add_argument("root_path", default='5', type=str,
+                        help='GPU.')
+    args = parser1.parse_args()
+    num_count = 0
+    VocToFineWithLabel(num_count,args.root_path, int(args.image_T))
 
-        #os.remove(xml_file)
-print(num_count)