Fixes and a few additional helper functions
- corrected the return type of a few functions to int - Add DFWINDOW.TOOLS.getImageDiff to get how diff 2 images are - Added complimentary lastNotBlack(X/Y) - Increase blindly chosen preset "bottom_to_ignore". Wasnt enough when full screen. - Fixed the sign/polarity of step sizes - First attempt at calculating the size of the map. Probably wrong. Will replace when writing the map maker - removed cv2.Stitcher stuff. Not what I needed - Added some test_ functions for experimenting. Ignore.
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@@ -1,3 +1,4 @@
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import json
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import time
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import timeit
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from pathlib import Path
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@@ -49,7 +50,7 @@ class DFWINDOW:
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logger.debug(f"Content origin ({content_x}, {content_y})")
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return (content_x, content_y)
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return (int(content_x), int(content_y))
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@staticmethod
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def isRightBorder(img, num_columns=20, border_threshold: int = 10) -> bool:
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@@ -61,6 +62,20 @@ class DFWINDOW:
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# Are all pixels in the strip "black"
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return not np.any(thresh)
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@staticmethod
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def getImageDiff(image1, image2, conversion=cv2.COLOR_BGR2GRAY) -> float:
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# Diff size, very dif img
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if image1.shape != image2.shape:
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return float("inf")
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grey1 = cv2.cvtColor(image1, conversion)
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grey2 = cv2.cvtColor(image2, conversion)
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# Apparently this is the Mean Squared Error (MES). Thanks Gemini (LLM)
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err = np.sum((grey1.astype("float") - grey2.astype("float")) ** 2)
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err /= float(grey1.shape[0] * grey1.shape[0])
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return float(err)
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@staticmethod
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def isLeftBorder(img, num_columns=20, border_threshold: int = 10) -> bool:
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# grab a greyscale strip to look at
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@@ -93,19 +108,29 @@ class DFWINDOW:
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@staticmethod
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def firstNotBlackX(img) -> int:
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first_x = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=0) > 15)[0][0]
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first_x = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=0) > 5)[0][0]
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return int(first_x)
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@staticmethod
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def lastNotBlackX(img) -> int:
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first_x = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=0) > 5)[0][-1]
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return int(first_x)
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@staticmethod
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def firstNotBlackY(img) -> int:
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first_y = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=1) > 15)[0][0]
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first_y = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=1) > 5)[0][0]
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return int(first_y)
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bottom_to_ignore = 120
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sleep_after_mouse = 0.2
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sleep_after_key = 0.08
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sleep_after_focus = 0.3
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sleep_after_panning = 0.3
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@staticmethod
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def lastNotBlackY(img) -> int:
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first_y = np.where(np.max(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), axis=1) > 5)[0][-1]
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return int(first_y)
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bottom_to_ignore = 160
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sleep_after_mouse = 0.2 # 2
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sleep_after_key = 0.08 # 08
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sleep_after_focus = 0.2 # 3
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sleep_after_panning = 0.2 # 3
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query_for_window = "dwarfort"
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def __init__(self) -> None:
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@@ -227,8 +252,8 @@ class DFWINDOW:
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self.sendKeys("a", 30)
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img = self.capWindow()
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self._content_left, self._content_top = self.TOOLS.find_content_origin(img)
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self._content_right = img.shape[1] - self._content_left
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self._content_bottom = img.shape[0] - self.bottom_to_ignore
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self._content_right = int(img.shape[1] - self._content_left)
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self._content_bottom = int(img.shape[0] - self.bottom_to_ignore)
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img = img[self._content_top : self._content_bottom, self._content_left : self._content_right] # pyright: ignore[reportOptionalSubscript]
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logger.debug(f"Content width {self.contentWidth}. Content height {self.contentHeight}.")
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@@ -242,8 +267,8 @@ class DFWINDOW:
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img = self.capContent()
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mx2 = self.TOOLS.firstNotBlackX(img)
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my2 = self.TOOLS.firstNotBlackY(img)
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self._step_size_x = mx2 - mx1
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self._step_size_y = my2 - my1
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self._step_size_x = mx1 - mx2
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self._step_size_y = my1 - my2
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logger.info(f"Step sizes calculated: x={self._step_size_x} and y={self._step_size_y}")
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self.sendKeys("w")
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self.sendKeys("a")
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@@ -306,13 +331,6 @@ class DFWINDOW:
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self._gridx = steps_right
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self._gridy = steps_down
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# TODO: Use seek tests to calculate mapsize in pixels
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# at (0,0) save left_edge_offset and top_edge_offset
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# at (max,max) save right_edge_offset and bottom_edge_offset
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# width = (contentWidth - l_e_o) + (gridyx_max * _step_size_x) - abs(r_e_o)
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# | <==|====|====|==> |
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# (max*size) is too far, so we subract the ofset/border from the right map edge
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# Test going to 0,0
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self.setGridPos(0, 0)
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time.sleep(self.sleep_after_panning)
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@@ -332,17 +350,46 @@ class DFWINDOW:
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logger.debug("Calibration error. Not at requested lower right of map")
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raise Exception("Calibration error. Not at requested lower right of map")
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cal_right_border = self.TOOLS.lastNotBlackX(img)
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cal_bottom_border = self.TOOLS.lastNotBlackY(img)
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# TODO: Use seek tests to calculate mapsize in pixels
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# at (0,0) save left_edge_offset and top_edge_offset
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# at (max,max) save right_edge_offset and bottom_edge_offset
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# width = (contentWidth - l_e_o) + (gridyx_max * _step_size_x) - abs(r_e_o)
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# | <==|====|====|==> |
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# (max*size) is too far, so we subract the ofset/border from the right map edge
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self._map_width = (
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(img.shape[1] - cal_left_border) # Grid x = 0
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+ ((self._gridx_max - 1) * self._step_size_x) # All the middle
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+ cal_right_border # grid x = max
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)
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self._map_height = (
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(img.shape[0] - cal_top_border) # Grid x = 0
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+ ((self._gridy_max - 1) * self._step_size_y) # All the middle
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+ cal_right_border # grid x = max
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)
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self.setGridPos(0, 0)
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logger.debug(f"Map dimensions calculated as {self._map_width} x {self._map_height}")
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logger.info(
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f"Grid calibration complete. Grid steps ({self._gridy_max + 1},{self._gridy_max + 1}), step sizes({self._step_size_x},{self._step_size_y})"
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)
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def test1(self):
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rawimg = cv2.imread("./test_img.png")
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# rawimg = cv2.imread("./test_img.png")
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rawimg = cv2.imread("grid_base_3.png")
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img = rawimg[100 : -self.bottom_to_ignore - 70, 65:-65]
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tlb = self.TOOLS.firstNotBlackX(img)
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ttb = self.TOOLS.firstNotBlackY(img)
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tt_setup = r"gc.enable() ; import cv2 ; import numpy as np ; timg = cv2.imread('./test_img.png', cv2.IMREAD_UNCHANGED)"
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tt_setup = (
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r"gc.enable() ; import cv2 ; import numpy as np ; timg = cv2.imread('./test_img.png', cv2.IMREAD_UNCHANGED)"
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)
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tt1 = timeit.Timer(
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"np.where(np.mean(cv2.cvtColor(timg, cv2.COLOR_BGR2GRAY), axis=0) > 15)[0][0]",
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setup=tt_setup,
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@@ -363,30 +410,57 @@ class DFWINDOW:
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logger.debug("Pause here for testing")
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def test_saveGrids(self):
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savedir = Path()
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savefile_base = "cached_grid"
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savefile_ext = "png"
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for x in range(0, self._gridx_max + 1):
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for y in range(0, self._gridy_max):
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self.setGridPos(x, y)
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time.sleep(self.sleep_after_panning)
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img = self.capContent()
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savefilename = savedir.joinpath(f"{savefile_base}_{x}_{y}.{savefile_ext}")
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cv2.imwrite(str(savefilename.resolve()), img)
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calib_info = {
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"gridx": int(self._gridx),
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"gridy": int(self._gridy),
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"gridx_max": int(self._gridx_max),
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"gridy_max": int(self._gridy_max),
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"step_size_x": int(self._step_size_x),
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"step_size_y": int(self._step_size_y),
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"content_top": int(self._content_top),
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"content_bottom": int(self._content_bottom),
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"content_left": int(self._content_left),
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"content_right": int(self._content_right),
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"map_height": int(self._map_height),
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"map_width": int(self._map_width),
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}
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with open("./calib_info.json", "w") as fh:
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json.dump(calib_info, fh)
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def test_loadCalib(self):
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with open("./calib_info.json") as fh:
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calib_info = json.load(fh)
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self._gridx = calib_info["gridx"]
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self._gridy = calib_info["gridy"]
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self._gridx_max = calib_info["gridx_max"]
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self._gridy_max = calib_info["gridy_max"]
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self._step_size_x = calib_info["step_size_x"]
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self._step_size_y = calib_info["step_size_y"]
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self._content_top = calib_info["content_top"]
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self._content_bottom = calib_info["content_bottom"]
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self._content_left = calib_info["content_left"]
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self._content_right = calib_info["content_right"]
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self._map_height = calib_info["map_height"]
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self._map_width = calib_info["map_width"]
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def getPanoramaMap(self):
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self.test1()
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return
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# self.test1()
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# return
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self.calibrateGrid()
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# Test getting pieces and stitching
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stitcher = cv2.Stitcher.create(cv2.STITCHER_SCANS)
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stitcher.setPanoConfidenceThresh(0.1) # Dont be confident
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imgs_in_row = []
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# Get a row
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self.setGridPos(0, 0)
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time.sleep(self.sleep_after_panning)
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for x in range(0, self.maxGridX + 1, 3):
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self.setGridPos(x, self.curGridY)
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time.sleep(self.sleep_after_panning)
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img = self.capContent()
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if img.shape[2] == 4:
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img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
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imgs_in_row.append(img)
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status, strip = stitcher.stitch(imgs_in_row)
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logger.debug(f"{len(imgs_in_row)} images. {status=} {strip=} {status == cv2.Stitcher_OK}")
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# self.test_saveGrids()
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# self.test_loadCalib()
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return None
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