From 9890a4834e6fa07ffb09aba49e7cb749706250a6 Mon Sep 17 00:00:00 2001
From: wangying <wangying@supervision.ltd>
Date: Mon, 5 Jun 2023 10:02:26 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0atm=E6=A3=80=E6=B5=8B?=
=?UTF-8?q?=E7=9B=B8=E5=85=B3=E4=BB=A3=E7=A0=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
BANK_atm/ATM_det_pipeline.py | 0
BANK_atm/cut_img_bbox.py | 16 ++
BANK_atm/mediapipe_det.py | 42 +++++
BANK_atm/pipeline1.py | 307 +++++++++++++++++++++++++++++++++++
BANK_atm/t.py | 52 ++++++
BANK_atm/yolov8_det.py | 50 ++++++
6 files changed, 467 insertions(+)
create mode 100644 BANK_atm/ATM_det_pipeline.py
create mode 100644 BANK_atm/cut_img_bbox.py
create mode 100644 BANK_atm/mediapipe_det.py
create mode 100644 BANK_atm/pipeline1.py
create mode 100644 BANK_atm/t.py
create mode 100644 BANK_atm/yolov8_det.py
diff --git a/BANK_atm/ATM_det_pipeline.py b/BANK_atm/ATM_det_pipeline.py
new file mode 100644
index 0000000..e69de29
diff --git a/BANK_atm/cut_img_bbox.py b/BANK_atm/cut_img_bbox.py
new file mode 100644
index 0000000..828b10d
--- /dev/null
+++ b/BANK_atm/cut_img_bbox.py
@@ -0,0 +1,16 @@
+
+
+def cut_img_bbox(img,bbox):
+
+ # blist = bbox.tolist()
+
+ x_min = bbox[0]
+ y_min = bbox[1]
+ x_max = bbox[2]
+ y_max = bbox[3]
+
+ obj_img = img[int(y_min):int(y_max), int(x_min):int(x_max)] #cv2裁剪出目标框中的图片
+ #保存图片
+ # cut_images = obj_img_path + '\\' + '%s_%s'%(img_name, count) + '.jpg'
+
+ return obj_img
\ No newline at end of file
diff --git a/BANK_atm/mediapipe_det.py b/BANK_atm/mediapipe_det.py
new file mode 100644
index 0000000..5977084
--- /dev/null
+++ b/BANK_atm/mediapipe_det.py
@@ -0,0 +1,42 @@
+
+from mediapipe.python.solutions import drawing_utils
+
+
+
+
+
+def analysis_mediapipe(images,hands,parameter):
+
+ results = hands.process(images)
+
+ re_list = []
+
+ if results.multi_hand_landmarks:
+ for hand_landmarks in results.multi_hand_landmarks:
+ # print(hand_landmarks)
+ point_list = drawing_utils.draw_landmarks(
+ images, hand_landmarks, parameter)
+
+ re_list.append(point_list)
+
+ # print(re_list)
+
+ return re_list
+
+# if __name__ == '__main__':
+
+# mp_drawing = drawing_utils
+# mp_hands = hands
+# frame = cv2.imread("E:/BANK_XZ/data_file/4654613.png")
+ # parameter = mp_hands.HAND_CONNECTIONS
+
+# hands = mp_hands.Hands(
+# static_image_mode=True,
+# max_num_hands=4,
+# min_detection_confidence=0.1,
+# min_tracking_confidence=0.1)
+
+# analysis_mediapipe(frame,hands)
+
+
+
diff --git a/BANK_atm/pipeline1.py b/BANK_atm/pipeline1.py
new file mode 100644
index 0000000..39e093b
--- /dev/null
+++ b/BANK_atm/pipeline1.py
@@ -0,0 +1,307 @@
+import cv2
+from tqdm import tqdm
+from ultralytics import YOLO
+from ultralytics.yolo.utils.plotting import Annotator
+import os
+
+import cv2
+from mediapipe.python.solutions import drawing_utils
+from mediapipe.python.solutions import hands
+import time
+import os
+import queue
+import threading
+
+from yolov8_det import analysis_yolov8
+from cut_img_bbox import cut_img_bbox
+from mediapipe_det import analysis_mediapipe
+
+
+
+
+class atm_det:
+
+ def __init__(self,imgPath,savePath,modellist):
+
+ self.imgPath = imgPath
+ self.savePath = savePath
+
+ self.imgList = os.listdir(self.imgPath)
+
+ #定义加载好的模型
+ self.model_person = modellist[0]
+ self.model_pp_hand = modellist[1]
+ self.model_blue = modellist[2]
+ self.model_screen = modellist[3]
+
+ self.media_hands = modellist[4]
+
+ # 队列
+ self.imgQueue1 = queue.Queue(maxsize=len(self.imgList))
+ self.imgQueue2 = queue.Queue(maxsize=len(self.imgList))
+ self.imgQueue3 = queue.Queue(maxsize=len(self.imgList))
+ self.imgQueue4 = queue.Queue(maxsize=len(self.imgList))
+ self.imgQueue5 = queue.Queue(maxsize=len(self.imgList))
+ self.imgQueue6 = queue.Queue(maxsize=len(self.imgList))
+
+ #线程
+ self.get_imgThread = threading.Thread(target=self.get_img)
+ self.get_person_resultThread = threading.Thread(target=self.get_person_result)
+ self.get_hand_landmarkerThread = threading.Thread(target=self.get_hand_landmarker)
+ self.get_blue_resultThread = threading.Thread(target=self.get_blue_result)
+ self.get_pph_resultThread = threading.Thread(target=self.get_pph_result)
+ self.analysis_handThread = threading.Thread(target=self.analysis_hand)
+ self.draw_imagesThread = threading.Thread(target=self.draw_images)
+ self.analysis_hand_blueThread = threading.Thread(target=self.analysis_hand_blue)
+ self.get_screen_resultThread = threading.Thread(target=self.get_screen_result)
+
+
+
+ def get_img(self):
+
+ for img in self.imgList:
+
+ imgpath = os.path.join(self.imgPath,img)
+ images = cv2.imread(imgpath)
+
+ imagesDict = {img:images}
+
+ self.imgQueue1.put(imagesDict)
+
+
+ def get_person_result(self):
+
+ while True:
+
+ if ~self.imgQueue1.empty():
+
+ imagesDict = self.imgQueue1.get()
+ images = list(imagesDict.values())[0]
+ imgname = list(imagesDict.keys())[0]
+
+ per_result = analysis_yolov8(images=images,
+ model_coco=self.model_person,
+ confidence=0.5
+ )
+
+ for per in per_result:
+
+ per_bbox = list(per.values())[0]
+
+ imgcut = cut_img_bbox(images,per_bbox)
+ imgcutDict = {imgname:{"imgcut":imgcut,"per":per}}
+
+ self.imgQueue2.put(imgcutDict)
+
+ def get_blue_result(self):
+ while True:
+
+ if ~self.imgQueue1.empty():
+
+ imagesDict = self.imgQueue1.get()
+ images = list(imagesDict.values())[0]
+ imgname = list(imagesDict.keys())[0]
+
+ blue_result = analysis_yolov8(images=images,
+ model_coco=self.model_blue,
+ confidence=0.5
+ )
+ blues_list = []
+ for blues in blue_result:
+
+ blue = list(blues.values())[0]
+ blues_list.append(blue)
+
+ if blues_list:
+ bluesDict = {imgname:blues_list}
+
+ self.imgQueue4.put(bluesDict)
+
+ def get_pph_result(self):
+ while True:
+
+ if ~self.imgQueue1.empty():
+
+ imagesDict = self.imgQueue1.get()
+ images = list(imagesDict.values())[0]
+ imgname = list(imagesDict.keys())[0]
+
+ blue_result = analysis_yolov8(images=images,
+ model_coco=self.model_pp_hand,
+ confidence=0.5
+ )
+ pph_list = []
+ for blues in blue_result:
+
+ blue = list(blues.values())[0]
+ pph_list.append(blue)
+
+ if pph_list:
+
+ pphDict = {imgname:pph_list}
+ self.imgQueue5.put(pphDict)
+
+ def get_hand_landmarker(self):
+ while True:
+
+ if ~self.imgQueue2.empty():
+
+ imgcutDict = self.imgQueue2.get()
+
+ imgcut = list(imgcutDict.values())[0]["imgcut"]
+
+ hand_landmarker_result = analysis_mediapipe(images=imgcut,
+ hands=self.media_hands,
+ parameter=hands.HAND_CONNECTIONS)
+
+ handDict = {"hand_landmarker_result":hand_landmarker_result}
+
+ list(imgcutDict.values())[0].update(handDict)
+
+ self.imgQueue3.put(imgcutDict)
+
+
+ def get_screen_result(self):
+ while True:
+ if ~self.imgQueue1.empty():
+
+ imagesDict = self.imgQueue1.get()
+ images = list(imagesDict.values())[0]
+ imgname = list(imagesDict.keys())[0]
+
+ screen_result = analysis_yolov8(images=images,
+ model_coco=self.model_screen,
+ confidence=0.5
+ )
+ print('screen_result:',screen_result)
+
+
+ def analysis_hand(self):
+ while True:
+
+ if ~self.imgQueue3.empty():
+
+ imgcutDict2 = self.imgQueue3.get()
+ imgname = list(imgcutDict2.keys())[0]
+
+ re_list = list(imgcutDict2.values())[0]
+
+ pre_list = re_list['per']
+ pre_list = list(pre_list.values())[0]
+
+ # pre_x = int(pre_list[2] - pre_list[0])
+ pre_x = int(pre_list[0])
+ pre_y = int(pre_list[1])
+ # pre_y = int(pre_list[3] - pre_list[1])
+
+ hand_list = re_list['hand_landmarker_result']
+
+ point_list = []
+ for hand_point in hand_list:
+
+ for point in hand_point:
+ # print(point)
+
+ point_x = int(point[0]) + pre_x
+ point_y = int(point[1]) + pre_y
+
+ point_list.append((point_x,point_y))
+
+ if point_list:
+ imgcutDict2.update({imgname:point_list})
+
+ self.imgQueue6.put(imgcutDict2)
+
+
+ def analysis_hand_blue(self):
+ while True:
+ if ~self.imgQueue4.empty() and ~self.imgQueue6.empty():
+
+ blue_list = self.imgQueue4.get()
+ hand_list = self.imgQueue6.get()
+
+ print('blue_list:',blue_list)
+ print('hand_list:',hand_list)
+
+ while list(blue_list.keys())[0] == list(hand_list.keys())[0]:
+ print(list(blue_list.keys())[0])
+
+
+
+
+
+
+
+ def draw_images(self):
+ while True:
+ if ~self.imgQueue6.empty():
+
+ img_hand_point = self.imgQueue6.get()
+ imgname = list(img_hand_point.keys())[0]
+
+ img = cv2.imread(os.path.join(self.imgPath,imgname))
+
+ point_list = list(img_hand_point.values())[0]
+
+ for point in point_list:
+
+ cv2.circle(img, point, 1,(0,0,255), 2)
+
+ cv2.imwrite(os.path.join(self.savePath,imgname),img)
+
+
+
+
+
+
+ def run(self):
+
+ self.get_imgThread.start()
+ self.get_person_resultThread.start()
+ self.get_hand_landmarkerThread.start()
+ self.get_blue_resultThread.start()
+ # self.get_pph_resultThread.start()
+ self.analysis_handThread.start()
+ # self.draw_imagesThread.start()
+ self.analysis_hand_blueThread.start()
+ self.get_screen_resultThread.start()
+
+
+
+if __name__ == '__main__':
+
+
+ model_person = YOLO("model_files/bk1.pt")
+ model_pp_hand = YOLO("model_files/best_pph.pt")
+ model_blue = YOLO("model_files/best_butten.pt")
+ model_screen = YOLO("model_files/best_screen.pt")
+
+
+ media_hands = hands.Hands(
+ static_image_mode=True,
+ max_num_hands=4,
+ min_detection_confidence=0.1,
+ min_tracking_confidence=0.1)
+
+ modelList = [model_person,model_pp_hand,model_blue,model_screen,media_hands]
+
+ q = atm_det(imgPath='E:/BANK_XZ/data_file',
+ savePath='E:/BANK_XZ/output_data',
+ modellist=modelList)
+
+ q.run()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/BANK_atm/t.py b/BANK_atm/t.py
new file mode 100644
index 0000000..be00104
--- /dev/null
+++ b/BANK_atm/t.py
@@ -0,0 +1,52 @@
+[{'imgcut': array([[[193, 194, 184],
+ [193, 194, 184],
+ [194, 198, 187],
+ ...,
+ [ 89, 92, 97],
+ [ 89, 92, 97],
+ [ 89, 92, 97]],
+
+ [[189, 188, 178],
+ [190, 191, 181],
+ [193, 197, 186],
+ ...,
+ [ 89, 92, 97],
+ [ 89, 92, 97],
+ [ 89, 92, 97]],
+
+ [[189, 187, 179],
+ [196, 195, 185],
+ [196, 197, 187],
+ ...,
+ [ 89, 92, 97],
+ [ 89, 92, 97],
+ [ 89, 92, 97]],
+
+ ...,
+
+ [[117, 131, 129],
+ [115, 131, 130],
+ [114, 132, 133],
+ ...,
+ [192, 189, 184],
+ [190, 191, 182],
+ [188, 191, 182]],
+
+ [[115, 131, 130],
+ [116, 132, 131],
+ [115, 133, 134],
+ ...,
+ [193, 190, 185],
+ [191, 192, 183],
+ [189, 192, 183]],
+
+ [[116, 132, 131],
+ [116, 132, 131],
+ [115, 133, 134],
+ ...,
+ [193, 190, 185],
+ [191, 192, 183],
+ [189, 192, 183]]], dtype=uint8),
+ 'per': {'wrong': [1006.59521484375, 429.05950927734375, 1710.3988037109375, 1078.1298828125]},
+ 'hand_landmarker_result': [[(175, 136), (246, 38), (195, 35), (164, 44), (169, 99)]]
+ }]
\ No newline at end of file
diff --git a/BANK_atm/yolov8_det.py b/BANK_atm/yolov8_det.py
new file mode 100644
index 0000000..b9139bf
--- /dev/null
+++ b/BANK_atm/yolov8_det.py
@@ -0,0 +1,50 @@
+import cv2
+from tqdm import tqdm
+from ultralytics import YOLO
+from ultralytics.yolo.utils.plotting import Annotator
+import os
+
+def analysis_yolov8(images, model_coco,confidence):
+
+ frame = images
+
+ # 第一步：用COCO数据集推理
+ results_coco = model_coco(frame)
+ # print(results_coco)
+
+ if results_coco:
+
+ for r in results_coco:
+
+ boxes = r.boxes
+
+ re_list = []
+ for box in boxes:
+
+ b = box.xyxy[0] # get box coordinates in (top, left, bottom, right) format
+ c = box.cls
+
+ # 保存标签和坐标值作为返回结果
+ blist = b.tolist()
+ labels_name = model_coco.names[int(c)]
+
+ confidence = float(box.conf)
+
+ confidence = round(confidence, 2)
+
+ # 过滤置信度0.5以下目标
+ if confidence < confidence:
+
+ continue
+
+ re_dict = {labels_name:blist}
+ re_list.append(re_dict)
+
+ return re_list
+
+
+# if __name__ == '__main__':
+
+# model_coco = YOLO("model_files/bk1.pt")
+# frame = cv2.imread("E:/BANK_XZ/data_file/0000162.jpg")
+# analysis_video(frame, model_coco,confidence=0.5)
\ No newline at end of file