7.4

detect.py

@@ -1,7 +1,7 @@
 import argparse
 import time
 from pathlib import Path
-import numpy as np
+
 import cv2
 import torch
 import torch.backends.cudnn as cudnn
@@ -61,14 +61,6 @@ def detect(opt, save_img=False):
         model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
     t0 = time.time()
     for path, img, im0s, vid_cap in dataset:
-        #MASK
-        site = np.array([[[0, 1024], [0, 571], [313, 365], [949, 367], [1277, 596], [1280, 1024]]], dtype=np.int32)
-        im = np.zeros(img.shape[:2], dtype="uint8")
-        cv2.polylines(im, site, 1, 255)
-        cv2.fillPoly(im, site, 255)
-        mask = im
-        masked = cv2.bitwise_or(img, img, mask=mask)
-        img = masked
         img = torch.from_numpy(img).to(device)
         img = img.half() if half else img.float()  # uint8 to fp16/32
         img /= 255.0  # 0 - 255 to 0.0 - 1.0
@@ -158,8 +150,7 @@ def detect(opt, save_img=False):
              "6923644272159", "6924882486100", "6956511907458"]
     targets = []
     for target in pred[0]:
-        targets.append({"Class": names[int(target[5].item())], "precision": target[4].item(),
-                        "xy1": [target[0].item(), target[1].item()],
+        targets.append({"Class": names[int(target[5].item())], "precision": target[4].item(), "xy1": [target[0].item(), target[1].item()],
                         "xy2": [target[2].item(), target[3].item()]})
     resu = {"TargetDetect": targets}
     print(resu)
@@ -197,4 +188,4 @@ if __name__ == '__main__':
 #                detect(opt)
 #                strip_optimizer(opt.weights)
 #        else:
-#            detect(opt,True)
+#            detect(opt,True)

ieemoo-ai-zhanting.py

@@ -78,6 +78,16 @@ def get_isempty():
         image_path = '../module/ieemoo-ai-zhanting/imgs/1.jpg'
         file = open(image_path, 'wb')
         file.write(imgdata)
+        # #mask
+        # img = cv2.imread(image_path)
+        # site = np.array([[[0, 1024], [0, 571], [313, 365], [949, 367], [1277, 596], [1280, 1024]]], dtype=np.int32)
+        # im = np.zeros(img.shape[:2], dtype="uint8")
+        # cv2.polylines(im, site, 1, 255)
+        # cv2.fillPoly(im, site, 255)
+        # mask = im
+        # masked = cv2.bitwise_or(img, img, mask=mask)
+        # img0 = masked
+        # cv2.imwrite("../module/ieemoo-ai-zhanting/imgs/1.jpg",img0)
         pred = detect.detect(opt)
         logger.info(pred)
         #print('pred', pred)