fix(single-mode): error when recognize status

fix #39

Makefile

@@ -1,9 +1,12 @@
-.PHONY: default test lint
+.PHONY: default test lint format
 
-default: test lint
+default: test format
 
 test:
 	py -3.8 -m pytest auto_derby
 
 lint:
 	py -3.8 -m black -t py38 --check --diff . 
+
+format:
+	py -3.8 -m black -t py38 . 

auto_derby/imagetools.py

@@ -11,7 +11,7 @@
 import cv2
 import cv2.img_hash
 import numpy as np
-from PIL.Image import Image, fromarray
+from PIL.Image import BICUBIC, BILINEAR, Image, fromarray
 
 
 def md5(b_img: np.ndarray, *, save_path: Optional[Text] = None) -> Text:
@@ -158,8 +158,10 @@ def mix(a: np.ndarray, b: np.ndarray, a_mix: float) -> np.ndarray:
     )
 
 
-def bg_mask_by_outline(outline_img: np.ndarray) -> np.ndarray:
-    h, w = outline_img.shape[:2]
+def border_flood_fill(
+    cv_img: np.ndarray, color: Tuple[int, ...] = (255,)
+) -> np.ndarray:
+    h, w = cv_img.shape[:2]
 
     border_points = (
         *((0, i) for i in range(h)),
@@ -168,27 +170,37 @@ def bg_mask_by_outline(outline_img: np.ndarray) -> np.ndarray:
         *((i, h - 1) for i in range(w)),
     )
 
-    fill_mask_img = cv2.copyMakeBorder(outline_img, 1, 1, 1, 1, cv2.BORDER_CONSTANT)
-    bg_mask_img = np.zeros_like(outline_img)
+    fill_mask_img = cv2.copyMakeBorder(cv_img, 1, 1, 1, 1, cv2.BORDER_CONSTANT)
+    bg_mask_img = np.zeros_like(cv_img)
     for i in border_points:
         x, y = i
-        if outline_img[y, x] != 0:
+        if cv_img[y, x] != 0:
             continue
-        cv2.floodFill(bg_mask_img, fill_mask_img, (x, y), (255,), 0, 0)
+        cv2.floodFill(bg_mask_img, fill_mask_img, (x, y), color, 0, 0)
 
     return bg_mask_img
 
 
-def resize_by_heihgt(img: Image, height: int) -> Image:
+def bg_mask_by_outline(outline_img: np.ndarray) -> np.ndarray:
+    return border_flood_fill(outline_img)
+
+
+def resize_by_heihgt(img: Image, height: int, *, resample: int = BICUBIC) -> Image:
     w, h = img.width, img.height
     w = round(height / h * w)
     h = height
-    return img.resize((w, h))
+    return img.resize((w, h), resample=resample)
 
 
-def fill_area(img: np.ndarray, color: Tuple[int, ...], *, size_lt: int):
+def fill_area(
+    img: np.ndarray,
+    color: Tuple[int, ...],
+    *,
+    mode: int = cv2.RETR_EXTERNAL,
+    size_lt: int,
+):
     contours, _ = cv2.findContours(
-        (img * 255).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+        (img * 255).astype(np.uint8), mode, cv2.CHAIN_APPROX_NONE
     )
     for i in contours:
         size = cv2.contourArea(i)

auto_derby/single_mode/context.py

@@ -1,18 +1,18 @@
 # -*- coding=UTF-8 -*-
 # pyright: strict
 from __future__ import annotations
-import cv2
-import numpy as np
 
+import os
 from typing import Text, Tuple
 
-from PIL.Image import Image
+import cast_unknown as cast
+import cv2
+import numpy as np
 import PIL.ImageOps
+from PIL.Image import Image
 from PIL.Image import fromarray as image_from_array
 
-from .. import ocr, imagetools, templates, template
-
-import os
+from .. import imagetools, ocr, template, templates
 
 
 def _ocr_date(img: Image) -> Tuple[int, int, int]:
@@ -90,41 +90,19 @@ def _recognize_status(img: Image) -> Tuple[int, Text]:
     cv_img = cv2.copyMakeBorder(cv_img, 4, 4, 4, 4, cv2.BORDER_CONSTANT, value=(255,))
 
     blurred_img = cv2.medianBlur(cv_img, 7)
-    binary_img = cv2.adaptiveThreshold(
+
+    text_img = cv2.adaptiveThreshold(
         blurred_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 15, -1
     )
-
-    contours, hierarchy = cv2.findContours(
-        binary_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
+    text_img = 255 - cast.instance(
+        np.maximum(text_img, imagetools.border_flood_fill(text_img)), np.ndarray
     )
-    if not contours:
-        raise ValueError("_recognize_status: image is empty")
-
-    text_img = np.zeros_like(binary_img)
-
-    def _contour_level(h: np.ndarray) -> int:
-        _, _, _, parent = h
-        if parent < 0:
-            return 0
-        return _contour_level(hierarchy[0][parent]) + 1
-
-    for index, h in enumerate(hierarchy[0]):
-        level = _contour_level(h)
-        if level < 2:
-            continue
-        is_white = level % 2 == 0
-        if is_white and cv2.contourArea(contours[index]) < 160:
-            continue
-        color = (255,) if is_white else (0,)
-        cv2.drawContours(text_img, contours, index, thickness=cv2.FILLED, color=color)
-        if color == (0,):
-            # use white border
-            cv2.drawContours(text_img, contours, index, thickness=1, color=(255,))
+    text_img = cv2.medianBlur(text_img, 5)
+    imagetools.fill_area(text_img, (0,), mode=cv2.RETR_LIST, size_lt=40)
 
     if os.getenv("DEBUG") == __name__:
         cv2.imshow("cv_img", cv_img)
         cv2.imshow("blurred_img", blurred_img)
-        cv2.imshow("binary_img", binary_img)
         cv2.imshow("text_img", text_img)
         cv2.waitKey()
         cv2.destroyAllWindows()

auto_derby/single_mode/context_test.py

@@ -256,3 +256,22 @@ def test_update_by_character_detail_4():
     assert ctx.head == ctx.STATUS_A, ctx.head
     assert ctx.middle == ctx.STATUS_A, ctx.middle
     assert ctx.last == ctx.STATUS_G, ctx.last
+
+
+def test_update_by_character_detail_issue39():
+    img = PIL.Image.open(_TEST_DATA_PATH / "character_detail_issue39.png").convert("RGB")
+    ctx = Context()
+    ctx.update_by_character_detail(img)
+
+    assert ctx.turf == ctx.STATUS_A, ctx.turf
+    assert ctx.dart == ctx.STATUS_F, ctx.dart
+
+    assert ctx.sprint == ctx.STATUS_F, ctx.sprint
+    assert ctx.mile == ctx.STATUS_C, ctx.mile
+    assert ctx.intermediate == ctx.STATUS_A, ctx.intermediate
+    assert ctx.long == ctx.STATUS_A, ctx.long
+
+    assert ctx.lead == ctx.STATUS_G, ctx.lead
+    assert ctx.head == ctx.STATUS_A, ctx.head
+    assert ctx.middle == ctx.STATUS_A, ctx.middle
+    assert ctx.last == ctx.STATUS_F, ctx.last

ocr_labels.json

@@ -205,12 +205,6 @@
   "0001c827fc7ffe7ffe7f8e6386418e418e61fe7ffeffbeff8c63800300030003": "中",
   "fefffefffeff803f801f800fc007c003e001e001f001f001f801f800f8006000": "7",
   "c003c003c003c003c003c003c007c007c007e00ff00ff83e3efc1ff80f700600": "人",
-  "e007e00fe00f600e700c781c781c383c383cfc3ffc3ffc7f1e700ef00ff007e0": "A",
-  "f83ffc3f3e381f000f0007000700077e07ff07fe07e00fe01fe0fefffc7ff03f": "G",
-  "c01ff8fffcff7ef01e000f000f000f000f000f000f001f003e007cf0fcfff07f": "C",
-  "ff073f3e0ff80ff00f700f38ff1fff1f3f3c0ff00fe00fc00fe00ff03f7cff1f": "B",
-  "ff0fff3f077f077803f003f003e003e003e003e003f00378077c0f3eff1fff03": "D",
-  "e01ff87ffefc3f700f000f000700030003fe03fe07e00fc01fe03ef0fcfff87f": "G",
   "8007f01ffc7f3cfc00f800fcc03fc01fc01fc07f00fc00f81efcfefffc7ff81f": "3",
   "f03ff07ff87820700078007c003ec01fe00ff001f800f800fc7ffc7ff87f0000": "2",
   "c001c001c001c001c001c001c003c003e007e007f01ff03f787e7ffc1ff81ff0": "人",
@@ -220,10 +214,6 @@
   "f03ff87ff8fc38f000f000f800fe803fc007e003f801f800f800f8fff8fff87f": "2",
   "f07ff8fff8fff07f003e001f000f800f800fc007c007c007e007f003e003e003": "7",
   "f07ff8ff7cf800f000f000fc007f003f00fc00f000f000f03cfcfcfff87fe01f": "3",
-  "0078ffff3f000f000f000f00df1fff3fff070f000f000f000f001f00fffffeff": "E",
-  "0700ff1f1f3c0f780ff00ff00fe00fc00fc00fe00ff00ff00f780f3eff1fff07": "D",
-  "ff03ff1f0f3e073c03380338071eff1fff1f077c03f803f003f8077cbf3fff0f": "B",
-  "ff03ff0f1f780ff80ff00f700f3cff1fff3f0ff00fe00fc00fe01ff87f7eff0f": "B",
   "0000f01ff87f7c7c0078007c003e001fe00ff003f800fc00fc3ffc7ff83f0000": "2",
   "0000e01ff83f783838003800f80ffc3ffc7e3c7838783878787cf03fe01f8007": "6",
   "c000c001c001fe3fff7fff7fc763c361e773ff7fff7fe773c001c001c001c000": "中",
@@ -288,13 +278,8 @@
   "e07ff0ffe0fc00f8007c803f807f00fe00f000f070f8f0fff03fc01f00000000": "3",
   "e00ff81f783f003c003e801fc01f803f007e00fc08fcfc7ff83ff01f00000000": "3",
   "0000f81ff83f387c007c003fc01fc03f007e007c00f87c7efc7ff03f80070000": "3",
-  "e007fc3f3e7f0f3c0f001f00ff00fc0fe03f007f00f800f00ff03f7cfe3ff007": "S",
-  "fe033f3f0f780f700f700e38fe1ffe3f3e7c0ef00ee00ee00ee01ef0fe3ffe0f": "B",
-  "8003e007e007f00ff81f781e381c3c1c3c3c7c3efe3ffe7f0f7007f007e000c0": "A",
-  "e00ff87f7c781e300f008707870f030e03fe03fe07f00fe01ee07cf0f8fff03f": "G",
   "e01ff87ff8fe30fc00fc007c003fc03f00fe00f800f000f0f8fff87ff03fc007": "3",
   "c03fe07f70207800781ff87ff87f78f078f078f0f8f0f07fe03f800f00000000": "6",
-  "f83ffc7f1e780e001e00fe01fc0ff83f807f00f800f006e01ff07efcfc7fe00f": "S",
   "0000f00ff81f703e003c003e801fc01f803f007e00fc18fcfc7efc3ff01fe007": "3",
   "e00ff03ff83c78183800f80ffc3ffc7f3c7838787878f83ff01fe00f00000000": "6",
   "e01ff87ffc7f38f000f8007c003fe01ff007f801fc00fc7ffc7ff87f00000000": "2",
@@ -375,14 +360,24 @@
   "f007f80ffc3f1e3e1f3c1f7c1ffc1ffc1ffc1ffc1f7c1f3c1e3efc3ff80ff003": "0",
   "fc3ffc7f7c003c003c00fc1ffc3ffe7f1c7800f000f018f87e7cfe7ffc3ff00f": "5",
   "c001f007fc1f3c3c1e781e781ef8fcfffcfff8ffc0f90078087cfc3ffc1ff807": "9",
-  "e00ff83f3c3c1e000f03c7036702677e27fe27fe07e00ee01ef3fc7ff83fe01f": "G",
-  "fc073f3f1f7c0f781f781f38ff1fff1f3f3c1f701fe00fe01ff03f78ff3ffe0f": "B",
-  "f301ff1f0f3f077c0770077007f007f007f007f007700778073c1f3fff0ffe01": "D",
-  "e200ff0f1f3f073c0738073c0f1eff1fff1f0f7c077807f807780f7eff1ffe03": "B",
   "0000ff3fff1f1f001f001f00ff1fff3f3f7c04f800f800fc1efcff7ffe1ff80f": "5",
   "e007fc1ffe3f1e781ef01ef01ef8fefffcfff0fb00f000781c3cfc3ff80fe003": "9",
   "0000003fc07ff07fe07f807f007e007f007f007f007f007f007f003e00000000": "1",
   "e003f80ffe3f1e7c1f781e78fc3ffc1ffe3ffe7f0ff80ff80f783f7cfc1ff007": "8",
   "0000fcfffc7f3c003e007e00ff3fffff3efc00f000f018f03ef8fefff83ff00f": "5",
-  "0003e01ff87f7c783f001f00ff1fff7f7ffe3ff81ff01ff03ef8fcfef87ff00f": "6"
+  "0003e01ff87f7c783f001f00ff1fff7f7ffe3ff81ff01ff03ef8fcfef87ff00f": "6",
+  "e007f00f380c381c981d98198c19cc338c310e300670e667e76773ee3ffc1e7c": "A",
+  "fe7fffe003e083ffc33fc301833103700378c37fc33fc300c300e700ff007e00": "F",
+  "fc3fffff07e087ffc77fe639463006600670c63fe61fe600660066007e007e00": "F",
+  "8007f03f3c700ee0c6ffc37f6330730073006300c77886ff0ee03c70f03f800f": "C",
+  "e00ff00f300c30189819981998399c338c710c600660e6e7e6cf37cc3ff81ef8": "A",
+  "c00ff03e38700c63c67fe63c673c237f23e363c7e6cfc6cf0ce01c60f87fe01f": "G",
+  "c007f83f1c700e63c67f623c227cb3e1b3c1e2c7e6cfc6c40ce03870f03fc007": "G",
+  "fc03ff1f073c8360c3e3c3e3c371037003e0c3c7c3c6c3c783e10370ff3ffe1f": "B",
+  "fe01ff0f031e4330f361f36333e633e633e633e63363f3614330031eff0ffe03": "D",
+  "e00ff83e1c700e63c77fe33c737cb3ff93c1f3c763cec3cf06c11c60f83fe01f": "G",
+  "fc3fffff07e083ffe37fe33f033803300338e31fe300e37f03e007e0fefffe7f": "E",
+  "fe0fff1f03304360e3e3e3c763ce63ce63ce63c6e3c7e3630370033cff1ffe07": "D",
+  "fe07fe1f0738c770e763e763c73107300660e6e366c7e6c3c6610778fe3ffc0f": "B",
+  "e003781e1e388771c37fc33f871f0e383c70fce1fec3e3e387600e78fc3ff00f": "S"
 }

typings/cv2/_contrib.pyi

@@ -29745,29 +29745,6 @@ def imwritemulti(filename, img, params=...) -> retval:
     """
     ...
 
-def inRange(src, lowerb, upperb, dst=...) -> dst:
-    """
-    .   @brief  Checks if array elements lie between the elements of two other arrays.
-    .
-    .   The function checks the range as follows:
-    .   -   For every element of a single-channel input array:
-    .       \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0\f]
-    .   -   For two-channel arrays:
-    .       \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0  \land \texttt{lowerb} (I)_1  \leq \texttt{src} (I)_1 \leq  \texttt{upperb} (I)_1\f]
-    .   -   and so forth.
-    .
-    .   That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
-    .   specified 1D, 2D, 3D, ... box and 0 otherwise.
-    .
-    .   When the lower and/or upper boundary parameters are scalars, the indexes
-    .   (I) at lowerb and upperb in the above formulas should be omitted.
-    .   @param src first input array.
-    .   @param lowerb inclusive lower boundary array or a scalar.
-    .   @param upperb inclusive upper boundary array or a scalar.
-    .   @param dst output array of the same size as src and CV_8U type.
-    """
-    ...
-
 def initCameraMatrix2D(objectPoints, imagePoints, imageSize, aspectRatio=...) -> retval:
     """
     .   @brief Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

typings/cv2/cv2.pyi

@@ -18403,7 +18403,9 @@ def imwritemulti(filename, img, params=...) -> retval:
     """
     ...
 
-def inRange(src, lowerb, upperb, dst=...) -> dst:
+def inRange(
+    src: ndarray, lowerb: ArrayLike, upperb: ArrayLike, dst: Optional[ndarray] = ...
+) -> ndarray:
     """
     .   @brief  Checks if array elements lie between the elements of two other arrays.
     .

typings/numpy.pyi

@@ -33466,7 +33466,20 @@ mgrid: ...
 <numpy.lib.index_tricks.MGridClass object>
 """
 
-def minimum(*args, **kwargs):
+def minimum(
+    x1: ndarray,
+    x2: ndarray,
+    /,
+    out: Optional[ndarray] = None,
+    *,
+    where: bool = True,
+    casting: Text = "same_kind",
+    order: Text = "K",
+    dtype: Optional[Type[Any]] = None,
+    subok: bool = True,
+    signature: Any = ...,
+    extobj: Any = ...,
+) -> Union[ndarray, Scalar]:
     """
     <ufunc 'minimum'>
     """