Improved the seeking in the grid calibration

- Make the seeking of the upper left corner more intelligent/responsive rather than hard coding "left 15 times up 15 times"
- Commented out more experimentation code

.gitignore

@@ -231,3 +231,4 @@ Thumbs.db
 # experiment garbage
 
 *.png
+src/calib_info.json

src/df_tools/dfwindow.py

@@ -1,3 +1,4 @@
+import json
 import time
 from pathlib import Path
 
@@ -8,6 +9,8 @@ from loguru import logger
 from .waytools import capActiveWindow, focusWindow, moveMouse
 from .waytools import sendKey as _sendKey
 
+# TODO: Consider type hinting images   from cv2.typing import MatLike
+
 
 class DFWINDOW:
     class TOOLS:
@@ -29,24 +32,24 @@ class DFWINDOW:
         ) -> tuple[int, int]:
             # Check the first (num_rows) rows at the top of the image,
             #   ignoring (ignore_cols) number of pixels at each end of teh line.
-            test_mean = np.mean(
+            test_max = np.max(
                 cv2.cvtColor(image_in[0:num_rows, ignore_cols:-ignore_cols], cv2.COLOR_BGR2GRAY),
                 axis=1,  # get the mean along the x-axis
             )
             # TODO: handle when 0 results return
             # Test the mean darkness, get the first row darker than 4
-            content_y = np.where(test_mean < mean_threshold)[0][0]
+            content_y = np.where(test_max < mean_threshold)[0][0]
 
             _ignore_rows = max(ignore_rows, content_y + 1)
-            test_mean = np.mean(
+            test_max = np.max(
                 cv2.cvtColor(image_in[_ignore_rows:-_ignore_rows, 0:num_cols], cv2.COLOR_BGR2GRAY),
                 axis=0,  # get the mean along the y-axis
             )
-            content_x = np.where(test_mean < mean_threshold)[0][0]
+            content_x = np.where(test_max < mean_threshold)[0][0]
 
             logger.debug(f"Content origin ({content_x}, {content_y})")
 
-            return (content_x, content_y)
+            return (int(content_x), int(content_y))
 
         @staticmethod
         def isRightBorder(img, num_columns=20, border_threshold: int = 10) -> bool:
@@ -58,6 +61,20 @@ class DFWINDOW:
             # Are all pixels in the strip "black"
             return not np.any(thresh)
 
+        @staticmethod
+        def getImageDiff(image1, image2, conversion=cv2.COLOR_BGR2GRAY) -> float:
+            # Diff size, very dif img
+            if image1.shape != image2.shape:
+                return float("inf")
+            grey1 = cv2.cvtColor(image1, conversion)
+            grey2 = cv2.cvtColor(image2, conversion)
+
+            # Apparently this is the Mean Squared Error (MES). Thanks Gemini (LLM)
+            err = np.sum((grey1.astype("float") - grey2.astype("float")) ** 2)
+            err /= float(grey1.shape[0] * grey1.shape[0])
+
+            return float(err)
+
         @staticmethod
         def isLeftBorder(img, num_columns=20, border_threshold: int = 10) -> bool:
             # grab a greyscale strip to look at
@@ -89,20 +106,30 @@ class DFWINDOW:
             return not np.any(thresh)
 
         @staticmethod
-        def firstNotBlackX(img):
-            first_x = np.where(np.mean(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=0) > 15)[0][0]
-            return first_x
+        def firstNotBlackX(img) -> int:
+            first_x = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=0) > 5)[0][0]
+            return int(first_x)
 
         @staticmethod
-        def firstNotBlackY(img):
-            first_y = np.where(np.mean(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=1) > 15)[0][0]
-            return first_y
+        def lastNotBlackX(img) -> int:
+            first_x = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=0) > 5)[0][-1]
+            return int(first_x)
 
-    bottom_to_ignore = 120
-    sleep_after_mouse = 0.2
-    sleep_after_key = 0.08
-    sleep_after_focus = 0.3
-    sleep_after_panning = 0.3
+        @staticmethod
+        def firstNotBlackY(img) -> int:
+            first_y = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=1) > 5)[0][0]
+            return int(first_y)
+
+        @staticmethod
+        def lastNotBlackY(img) -> int:
+            first_y = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=1) > 5)[0][-1]
+            return int(first_y)
+
+    bottom_to_ignore = 160
+    sleep_after_mouse = 0.2  # 2
+    sleep_after_key = 0.08  # 08
+    sleep_after_focus = 0.2  # 3
+    sleep_after_panning = 0.2  # 3
     query_for_window = "dwarfort"
 
     def __init__(self) -> None:
@@ -220,12 +247,26 @@ class DFWINDOW:
         time.sleep(self.sleep_after_mouse)
         self.focusWindow()
         time.sleep(self.sleep_after_focus)
-        self.sendKeys("w", 30)
-        self.sendKeys("a", 30)
+
+        # Improved seeking upper left
+        self.sendKeys("w", 8)
+        img = self.capContent()
+        while not self.TOOLS.isTopBorder(img):
+            self.sendKeys("w", 4)
+            img = self.capContent()
+        self.sendKeys("w", 4)
+        
+        self.sendKeys("a", 8)
+        img = self.capContent()
+        while not self.TOOLS.isLeftBorder(img):
+            self.sendKeys("a", 4)
+            img = self.capContent()
+        self.sendKeys("a", 4)
+
         img = self.capWindow()
         self._content_left, self._content_top = self.TOOLS.find_content_origin(img)
-        self._content_right = img.shape[1] - self._content_left
-        self._content_bottom = img.shape[0] - self.bottom_to_ignore
+        self._content_right = int(img.shape[1] - self._content_left)
+        self._content_bottom = int(img.shape[0] - self.bottom_to_ignore)
         img = img[self._content_top : self._content_bottom, self._content_left : self._content_right]  # pyright: ignore[reportOptionalSubscript]
         logger.debug(f"Content width {self.contentWidth}. Content height {self.contentHeight}.")
 
@@ -239,8 +280,8 @@ class DFWINDOW:
         img = self.capContent()
         mx2 = self.TOOLS.firstNotBlackX(img)
         my2 = self.TOOLS.firstNotBlackY(img)
-        self._step_size_x = mx2 - mx1
-        self._step_size_y = my2 - my1
+        self._step_size_x = mx1 - mx2
+        self._step_size_y = my1 - my2
         logger.info(f"Step sizes calculated: x={self._step_size_x} and y={self._step_size_y}")
         self.sendKeys("w")
         self.sendKeys("a")
@@ -303,13 +344,6 @@ class DFWINDOW:
         self._gridx = steps_right
         self._gridy = steps_down
 
-        # TODO: Use seek tests to calculate mapsize in pixels
-        #   at (0,0) save left_edge_offset and top_edge_offset
-        #   at (max,max) save right_edge_offset and bottom_edge_offset
-        #   width = (contentWidth - l_e_o) + (gridyx_max * _step_size_x) - abs(r_e_o)
-        #   | <==|====|====|==>  |
-        #   (max*size) is too far, so we subract the ofset/border from the right map edge
-
         # Test going to 0,0
         self.setGridPos(0, 0)
         time.sleep(self.sleep_after_panning)
@@ -318,6 +352,9 @@ class DFWINDOW:
             logger.debug("Calibration error. Not at requested upper left of map")
             raise Exception("Calibration error. Not at requested upper left of map")
 
+        cal_left_border = self.TOOLS.firstNotBlackX(img)
+        cal_top_border = self.TOOLS.firstNotBlackY(img)
+
         # Test going to (max,max)
         self.setGridPos(self.maxGridX, self.maxGridY)
         time.sleep(self.sleep_after_panning)
@@ -326,31 +363,112 @@ class DFWINDOW:
             logger.debug("Calibration error. Not at requested lower right of map")
             raise Exception("Calibration error. Not at requested lower right of map")
 
+        cal_right_border = self.TOOLS.lastNotBlackX(img)
+        cal_bottom_border = self.TOOLS.lastNotBlackY(img)
+
+        # TODO: Use seek tests to calculate mapsize in pixels
+        #   at (0,0) save left_edge_offset and top_edge_offset
+        #   at (max,max) save right_edge_offset and bottom_edge_offset
+        #   width = (contentWidth - l_e_o) + (gridyx_max * _step_size_x) - abs(r_e_o)
+        #   | <==|====|====|==>  |
+        #   (max*size) is too far, so we subract the ofset/border from the right map edge
+
+        self._map_width = (
+            (img.shape[1] - cal_left_border)  # Grid x = 0
+            + ((self._gridx_max - 1) * self._step_size_x)  # All the middle
+            + cal_right_border  # grid x = max
+        )
+
+        self._map_height = (
+            (img.shape[0] - cal_top_border)  # Grid x = 0
+            + ((self._gridy_max - 1) * self._step_size_y)  # All the middle
+            + cal_bottom_border  # grid x = max
+        )
+
+        self.setGridPos(0, 0)
+
+        logger.debug(f"Map dimensions calculated as {self._map_width} x {self._map_height}")
+
         logger.info(
             f"Grid calibration complete. Grid steps ({self._gridy_max + 1},{self._gridy_max + 1}), step sizes({self._step_size_x},{self._step_size_y})"
         )
 
+    def test1(self):
+        # rawimg = cv2.imread("./test_img.png")
+        # rawimg = cv2.imread("grid_base_3.png")
+        # img = rawimg[100 : -self.bottom_to_ignore - 70, 65:-65]
+        # tlb = self.TOOLS.firstNotBlackX(img)
+        # ttb = self.TOOLS.firstNotBlackY(img)
+
+        # tt_setup = (
+        #     r"gc.enable() ; import cv2 ; import numpy as np ; timg = cv2.imread('./test_img.png', cv2.IMREAD_UNCHANGED)"
+        # )
+        # tt1 = timeit.Timer(
+        #     "np.where(np.mean(cv2.cvtColor(timg, cv2.COLOR_BGR2GRAY), axis=0) > 15)[0][0]",
+        #     setup=tt_setup,
+        # )
+        # tt2 = timeit.Timer(
+        #     "np.where(np.max(cv2.cvtColor(timg, cv2.COLOR_BGR2GRAY), axis=0) > 25)[0][0]",
+        #     setup=tt_setup,
+        # )
+        # tt3 = timeit.Timer(
+        #     "np.where(np.max(cv2.cvtColor(timg, cv2.COLOR_BGRA2GRAY), axis=0) > 25)[0][0]",
+        #     setup=tt_setup,
+        # )
+
+        # num_tests = 80
+        # r1 = tt1.timeit(number=num_tests)
+        # r2 = tt2.timeit(number=num_tests)
+        # r3 = tt3.timeit(number=num_tests)
+
+        logger.debug("Pause here for testing")
+
+    def test_saveGrids(self):
+        savedir = Path()
+        savefile_base = "cached_grid"
+        savefile_ext = "png"
+        for x in range(0, self._gridx_max + 1):
+            for y in range(0, self._gridy_max):
+                self.setGridPos(x, y)
+                time.sleep(self.sleep_after_panning)
+                img = self.capContent()
+                savefilename = savedir.joinpath(f"{savefile_base}_{x}_{y}.{savefile_ext}")
+                cv2.imwrite(str(savefilename.resolve()), img)
+        calib_info = {
+            "gridx": int(self._gridx),
+            "gridy": int(self._gridy),
+            "gridx_max": int(self._gridx_max),
+            "gridy_max": int(self._gridy_max),
+            "step_size_x": int(self._step_size_x),
+            "step_size_y": int(self._step_size_y),
+            "content_top": int(self._content_top),
+            "content_bottom": int(self._content_bottom),
+            "content_left": int(self._content_left),
+            "content_right": int(self._content_right),
+            "map_height": int(self._map_height),
+            "map_width": int(self._map_width),
+        }
+        with open("./calib_info.json", "w") as fh:
+            json.dump(calib_info, fh)
+
+    def test_loadCalib(self):
+        with open("./calib_info.json") as fh:
+            calib_info = json.load(fh)
+
+        self._gridx = calib_info["gridx"]
+        self._gridy = calib_info["gridy"]
+        self._gridx_max = calib_info["gridx_max"]
+        self._gridy_max = calib_info["gridy_max"]
+        self._step_size_x = calib_info["step_size_x"]
+        self._step_size_y = calib_info["step_size_y"]
+        self._content_top = calib_info["content_top"]
+        self._content_bottom = calib_info["content_bottom"]
+        self._content_left = calib_info["content_left"]
+        self._content_right = calib_info["content_right"]
+        self._map_height = calib_info["map_height"]
+        self._map_width = calib_info["map_width"]
+
     def getPanoramaMap(self):
         self.calibrateGrid()
 
-        # Test getting pieces and stitching
-        stitcher = cv2.Stitcher.create(cv2.STITCHER_SCANS)
-        stitcher.setPanoConfidenceThresh(0.1)  # Dont be confident
-
-        imgs_in_row = []
-
-        # Get a row
-        self.setGridPos(0, 0)
-        time.sleep(self.sleep_after_panning)
-        for x in range(0, self.maxGridX + 1, 3):
-            self.setGridPos(x, self.curGridY)
-            time.sleep(self.sleep_after_panning)
-            img = self.capContent()
-            if img.shape[2] == 4:
-                img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
-            imgs_in_row.append(img)
-
-        status, strip = stitcher.stitch(imgs_in_row)
-        logger.debug(f"{len(imgs_in_row)} images. {status=}  {strip=} {status == cv2.Stitcher_OK}")
-
         return None

src/df_tools/imgtools.py

@@ -2,8 +2,6 @@ import json
 import subprocess
 
 import pydantic as pyd
-import cv2
-import numpy as np
 
 from .dfwindow import DFWINDOW
 from .mylogging import logger, setup_logging
@@ -562,5 +560,5 @@ def test5():
 
 if __name__ == "__main__":
     setup_logging(level="DEBUG", enqueue=False, console_show_time=False, console_tracebacks=True)
-    
+
     test5()