focss edit on laptop

Author: focss-547

.gitattributes

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+*.traineddata filter=lfs diff=lfs merge=lfs binary
+*.xlsx filter=lfs diff=lfs merge=lfs binary
+*.png filter=lfs diff=lfs merge=lfs binary
+*.xml filter=lfs diff=lfs merge=lfs text

.gitignore

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+~*

BigData/BigData_FiringList.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2bbae5f146b8ea410785ce61373a7bb8220fc82b10fc834fed6d75ad093b5205
+size 5709

Ref/Ammo/0.png

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61b15d41411a5982b1275b8cafd260bea65259aeb6c42eb704898c6e6cb0294d
+size 15400601

Ref/Ammo/1.png

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+import cv2
+import numpy as np
+import os
+from func_cv_imread import cv_imread
+from func_img_proc import img_similarity, black_area
+import pandas as pd
+
+
+def cut_ammo(img: np.ndarray) -> np.ndarray:  # 裁切主弹夹弹药数字
+    img_cut = img[961:1000, 1700:1785]
+    return img_cut
+
+
+def binary_ammo(img: np.ndarray) -> np.ndarray:  # 弹药数字图二值化
+    img_binary = cv2.threshold(img, 215, 255, cv2.THRESH_BINARY_INV)[1]
+    return img_binary
+
+
+def ammo_ocr_img_process(img: np.ndarray) -> np.ndarray:  # 预处理弹药数字图
+    if img.ndim > 2:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    return binary_ammo(cut_ammo(img))
+
+
+def digit2(img: np.ndarray) -> np.ndarray:  # 百位数
+    return img[3:34, 3:26]
+
+
+def digit1(img: np.ndarray) -> np.ndarray:  # 十位数
+    return img[3:34, 30:53]
+
+
+def digit0(img: np.ndarray) -> np.ndarray:  # 个位数
+    return img[3:34, 57:80]
+
+
+def ammo_recognize_1_digit(img: np.ndarray) -> int:
+    if black_area(img) < 80:
+        return None
+    reffiledir = './Ref/Ammo/'
+    maxn = 10
+    similarity = np.zeros([maxn, 1], dtype=np.uint16)
+    for i in range(maxn):
+        img_Ref = cv2.imread(reffiledir + str(i) + '.png', 0)
+        sim = img_similarity(img, img_Ref)
+        similarity[i, 0] = sim
+    maxsimnum = np.max(similarity)
+    maxsimname = np.where(similarity == np.max(similarity))[0][0]
+    if maxsimnum < 300:
+        return None
+    return maxsimname
+
+
+def ammo_recognize_cv(img: np.ndarray, digits: int) -> int:
+    img = ammo_ocr_img_process(img)
+    ammo_num = ammo_recognize_1_digit(digit0(img))
+    if ammo_num is None:
+        return None
+    if digits > 1:
+        d1 = ammo_recognize_1_digit(digit1(img))
+        if d1 is not None:
+            ammo_num += 10 * d1
+    if digits == 3:
+        d2 = ammo_recognize_1_digit(digit2(img))
+        if d2 is not None:
+            ammo_num += 100 * d2
+    return ammo_num
+
+
+if __name__ == '__main__':
+    # Ammo_Train_Generate()
+    pass