add some opencv stuff

requirements.txt

@@ -2,3 +2,4 @@ prompt_toolkit
 pyautogui
 pywinctl
 opencv-python
+numpy

src/visual/main.py

@@ -0,0 +1,98 @@
+import pyautogui
+import colorsys
+import pywinctl
+import numpy
+import cv2 as cv
+import struct
+import matching
+from prompt_toolkit import prompt
+from prompt_toolkit.completion import WordCompleter
+
+# get all windows
+titles = pywinctl.getAllTitles()
+title_completer = WordCompleter(titles)
+
+try:
+    window = pywinctl.getWindowsWithTitle("Keep Talking and Nobody Explodes")[0]
+except IndexError:
+    try:
+        text = prompt("Select window name> ", completer=title_completer)
+        window = pywinctl.getWindowsWithTitle(text)[0]
+    except IndexError:
+        print("invalid title")
+        exit()
+
+if not window.isVisible:
+    window.show()
+
+frame = window.getClientFrame()
+print(frame)
+
+def get_image(left, top, right, bottom): # returns an opencv image
+    image = pyautogui.screenshot(region=(
+        left, top, right - left, bottom - top
+    ))
+
+    numpy_array = numpy.array(image)
+    opencv_image = cv.cvtColor(numpy_array, cv.COLOR_RGB2BGR)
+
+    return opencv_image
+
+rgb_colors_to_match = [
+    (220, 162, 129),
+    (220, 162, 129),
+    (145, 108, 89),
+]
+num_colors_in_range = 9
+delta_h_value = 10
+delta_s_value = 50
+delta_v_value = 50
+tolerence_value = 20
+
+ranges = [
+    matching.generate_hex_range(i, num_colors_in_range, delta_h=delta_h_value, delta_s=delta_s_value, delta_v=delta_v_value)
+    for i in rgb_colors_to_match
+]
+
+def are_rectangles_close(rect1, rect2):
+    distance_threshold = 20
+    x1, y1, w1, h1 = rect1
+    x2, y2, w2, h2 = rect2
+    return abs(x2 - x1) < distance_threshold and abs(y2 - y1) < distance_threshold
+
+while True:
+    image = get_image(frame.left, frame.top, frame.right, frame.bottom)
+    blank = numpy.zeros(image.shape, dtype='uint8')
+
+    combined = image
+    matched = matching.color_matching(image, ranges[0])
+    combined = matching.convert_masked_pixels_to_zero(combined, matched)
+    matched = matching.color_matching(image, ranges[1])
+    combined = matching.convert_masked_pixels_to_zero(combined, matched)
+    matched = matching.color_matching(image, ranges[2])
+    combined = matching.convert_masked_pixels_to_zero(combined, matched)
+
+    min_contour_length = 100
+    max_contour_length = 10000
+
+
+    bw = cv.cvtColor(combined, cv.COLOR_BGR2GRAY)
+    ret, thresh = cv.threshold(bw, 127, 255, cv.THRESH_BINARY)
+    contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    filtered_contours = [cnt for cnt in contours if min_contour_length <= cv.arcLength(cnt, True) <= max_contour_length]
+    monster_contour = numpy.concatenate(filtered_contours)
+    hull = cv.convexHull(monster_contour)
+    bounding_quad = cv.minAreaRect(hull)
+    points = cv.boxPoints(bounding_quad).astype(int)
+    print(points)
+
+    cv.drawContours(combined, [points], 0, (0, 255, 0), 2)
+
+    cv.drawContours(combined, filtered_contours, -1, (0,255,255), 2)
+
+    cv.imshow('test', combined)
+    key = cv.waitKey(1)
+    if (key) == 113: # q key
+        break
+
+cv.destroyAllWindows()

src/visual/matching.py

@@ -0,0 +1,59 @@
+import cv2
+import numpy as np
+
+def rgb_to_hsv(rgb_color):
+    # Convert RGB color to HSV color space
+    hsv_color = cv2.cvtColor(np.uint8([[rgb_color]]), cv2.COLOR_RGB2HSV)[0][0]
+    return hsv_color
+
+def generate_hex_range(rgb_color, num_colors, delta_h=10, delta_s=50, delta_v=50):
+    # Convert the RGB color to HSV color space
+    hsv_color = rgb_to_hsv(rgb_color)
+
+    # Define ranges in HSV color space
+    h_range = np.linspace(hsv_color[0] - delta_h, hsv_color[0] + delta_h, num_colors)
+    s_range = np.linspace(hsv_color[1] - delta_s, hsv_color[1] + delta_s, num_colors)
+    v_range = np.linspace(hsv_color[2] - delta_v, hsv_color[2] + delta_v, num_colors)
+
+    hex_range = []
+
+    # Generate colors in the HSV color space and convert them to BGR
+    for h_val in h_range:
+        for s_val in s_range:
+            for v_val in v_range:
+                hsv_color_modified = np.array([h_val, s_val, v_val])
+                bgr_color_modified = cv2.cvtColor(np.uint8([[hsv_color_modified]]), cv2.COLOR_HSV2BGR)[0][0]
+                hex_color = "#{:02x}{:02x}{:02x}".format(int(bgr_color_modified[2]),
+                                                         int(bgr_color_modified[1]),
+                                                         int(bgr_color_modified[0]))
+                hex_range.append(hex_color)
+
+    return hex_range
+
+def color_matching(image, hex_range_colors, tolerance=20):
+    hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+    matched_pixels = np.zeros_like(hsv_image[:, :, 0], dtype=np.uint8)
+
+    for hex_color in hex_range_colors:
+        # Convert the hex color to RGB
+        rgb_color = tuple(int(hex_color[i:i + 2], 16) for i in (1, 3, 5))
+        # Convert the RGB color to HSV
+        hsv_color = rgb_to_hsv(rgb_color)
+
+        lower_bound = np.array([hsv_color[0] - tolerance, hsv_color[1] - tolerance, hsv_color[2] - tolerance])
+        upper_bound = np.array([hsv_color[0] + tolerance, hsv_color[1] + tolerance, hsv_color[2] + tolerance])
+
+        mask = cv2.inRange(hsv_image, lower_bound, upper_bound)
+        matched_pixels |= mask
+
+    return matched_pixels
+
+def convert_masked_pixels_to_zero(image, mask):
+    # Create an all-white (ones) image with the same shape as the original image
+    white_image = np.ones_like(image) * 255
+
+    # Use the mask to set all masked pixels in the original image to zero
+    result_image = cv2.bitwise_and(image, white_image, mask=cv2.bitwise_not(mask))
+
+    return result_image