后端更新

2026-03-26 17:48:20 +08:00
parent 4c2bcd7dce
commit 0c2859b0db
22 changed files with 1336 additions and 213 deletions
@@ -0,0 +1,134 @@
+import os
+import time
+
+import cv2
+import numpy as np
+import torch
+
+from ai.plate.plate_recognition.plateNet import myNet_ocr_color
+
+
+def cv_imread(path):  # 可以读取中文路径的图片
+    img = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
+    return img
+
+
+def allFilePath(rootPath, allFIleList):
+    fileList = os.listdir(rootPath)
+    for temp in fileList:
+        if os.path.isfile(os.path.join(rootPath, temp)):
+            if temp.endswith(".jpg") or temp.endswith(".png") or temp.endswith(".JPG"):
+                allFIleList.append(os.path.join(rootPath, temp))
+        else:
+            allFilePath(os.path.join(rootPath, temp), allFIleList)
+
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+color = ["黑色", "蓝色", "绿色", "白色", "黄色"]
+plateName = r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品"
+mean_value, std_value = (0.588, 0.193)
+
+
+def decodePlate(preds):
+    pre = 0
+    newPreds = []
+    index = []
+    for i in range(len(preds)):
+        if preds[i] != 0 and preds[i] != pre:
+            newPreds.append(preds[i])
+            index.append(i)
+        pre = preds[i]
+    return newPreds, index
+
+
+def image_processing(img, device):
+    img = cv2.resize(img, (168, 48))
+    img = np.reshape(img, (48, 168, 3))
+
+    # normalize
+    img = img.astype(np.float32)
+    img = (img / 255.0 - mean_value) / std_value
+    img = img.transpose([2, 0, 1])
+    img = torch.from_numpy(img)
+
+    img = img.to(device)
+    img = img.view(1, *img.size())
+    return img
+
+
+def get_plate_result(img, device, model, is_color=False):
+    input = image_processing(img, device)
+    if is_color:  # 是否识别颜色
+        preds, color_preds = model(input)
+        color_preds = torch.softmax(color_preds, dim=-1)
+        color_conf, color_index = torch.max(color_preds, dim=-1)
+        color_conf = color_conf.item()
+    else:
+        preds = model(input)
+    preds = torch.softmax(preds, dim=-1)
+    prob, index = preds.max(dim=-1)
+    index = index.view(-1).detach().cpu().numpy()
+    prob = prob.view(-1).detach().cpu().numpy()
+
+    # preds=preds.view(-1).detach().cpu().numpy()
+    newPreds, new_index = decodePlate(index)
+    prob = prob[new_index]
+    plate = ""
+    for i in newPreds:
+        plate += plateName[i]
+    # if not (plate[0] in plateName[1:44] ):
+    #     return ""
+    if is_color:
+        return (
+            plate,
+            prob,
+            color[color_index],
+            color_conf,
+        )  # 返回车牌号以及每个字符的概率,以及颜色，和颜色的概率
+    else:
+        return plate, prob
+
+
+def init_model(device, model_path, is_color=False):
+    # print( print(sys.path))
+    # model_path ="plate_recognition/model/checkpoint_61_acc_0.9715.pth"
+    check_point = torch.load(model_path, map_location=device)
+    model_state = check_point["state_dict"]
+    cfg = check_point["cfg"]
+    color_classes = 0
+    if is_color:
+        color_classes = 5  # 颜色类别数
+    model = myNet_ocr_color(
+        num_classes=len(plateName), export=True, cfg=cfg, color_num=color_classes
+    )
+
+    model.load_state_dict(model_state, strict=False)
+    model.to(device)
+    model.eval()
+    return model
+
+
+# model = init_model(device)
+if __name__ == "__main__":
+    model_path = r"weights/plate_rec_color.pth"
+    image_path = "images/tmp2424.png"
+    testPath = r"/mnt/Gpan/Mydata/pytorchPorject/CRNN/crnn_plate_recognition/images"
+    fileList = []
+    allFilePath(testPath, fileList)
+    #    result = get_plate_result(image_path,device)
+    #    print(result)
+    is_color = False
+    model = init_model(device, model_path, is_color=is_color)
+    right = 0
+    begin = time.time()
+
+    for imge_path in fileList:
+        img = cv2.imread(imge_path)
+        if is_color:
+            plate, _, plate_color, _ = get_plate_result(
+                img, device, model, is_color=is_color
+            )
+            print(plate)
+        else:
+            plate, _ = get_plate_result(img, device, model, is_color=is_color)
+            print(plate, imge_path)