mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-11-22 16:40:28 +00:00
121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
import os
|
|
import cv2
|
|
import numpy as np
|
|
|
|
|
|
class Detector:
|
|
def __init__(self):
|
|
"""
|
|
A detector to detect the circle mark of enemy and item.
|
|
"""
|
|
self.ui_mask = cv2.imread(os.path.join(os.path.dirname(__file__), "mask.png"), 0)
|
|
|
|
def update(self, frame):
|
|
# apply a mask to every frame to block the UI from interfering detection
|
|
frame_masked = cv2.bitwise_and(frame, frame, mask=self.ui_mask)
|
|
# update the detector with current frame before detection
|
|
self.hsv = cv2.cvtColor(frame_masked, cv2.COLOR_BGR2HSV)
|
|
|
|
def detect_item(self):
|
|
filter_params = {
|
|
'lowerb': np.array([0, 0, 215]),
|
|
'upperb': np.array([108, 53, 255]),
|
|
}
|
|
hough_params = {
|
|
'minDist': 8,
|
|
'param1': 200,
|
|
'param2': 8,
|
|
'minRadius': 2,
|
|
'maxRadius': 4,
|
|
}
|
|
|
|
# augment the target marker through mask based on HSV color space
|
|
mask = cv2.inRange(self.hsv, **filter_params)
|
|
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)), iterations=1)
|
|
|
|
# resize the mask to reduce computational complexity of hough operation
|
|
mask = cv2.resize(mask, None, fx=0.25, fy=0.25)
|
|
mask = cv2.GaussianBlur(mask, (3, 3), 0, 0)
|
|
|
|
circles = cv2.HoughCircles(mask, cv2.HOUGH_GRADIENT, 1, **hough_params)
|
|
|
|
# if any target exists, return the center coordinates
|
|
return self.get_coordinates(circles) if circles is not None else None
|
|
|
|
def detect_enemy(self):
|
|
filter_params = {
|
|
'lowerb': np.array([0, 62, 213]),
|
|
'upperb': np.array([180, 198, 255]),
|
|
}
|
|
hough_params = {
|
|
'minDist': 18,
|
|
'param1': 200,
|
|
'param2': 10,
|
|
'minRadius': 5,
|
|
'maxRadius': 9,
|
|
}
|
|
|
|
mask = cv2.inRange(self.hsv, **filter_params)
|
|
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)), iterations=1)
|
|
|
|
mask = cv2.resize(mask, None, fx=0.25, fy=0.25)
|
|
mask = cv2.GaussianBlur(mask, (3, 3), 0, 0)
|
|
circles = cv2.HoughCircles(mask, cv2.HOUGH_GRADIENT, 1, **hough_params)
|
|
|
|
return self.get_coordinates(circles) if circles is not None else None
|
|
|
|
@staticmethod
|
|
def get_coordinates(circles):
|
|
# convert coordinates to original scale
|
|
circles = np.asarray(np.around(circles * 4), dtype=np.uint16).squeeze(0)
|
|
return [circle[:2] for circle in circles]
|
|
|
|
|
|
def generate_ui_mask():
|
|
"""
|
|
code to generate ui mask
|
|
"""
|
|
mask = np.ones([720, 1280]) * 255
|
|
mask[34:81, 21:61] = 0
|
|
mask[179:220, 21:51] = 0
|
|
mask[35:84, 183:218] = 0
|
|
mask[0:61, 780:1280] = 0
|
|
mask[145:435, 1153:1240] = 0
|
|
cv2.circle(mask, (907, 614), 55, 0, -1)
|
|
cv2.circle(mask, (1033, 542), 67, 0, -1)
|
|
cv2.imwrite("mask.png", mask)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# how to use
|
|
class YourClass:
|
|
def __init__(self, stream):
|
|
self.stream = stream
|
|
self.detector = Detector() # initiate a detector, recommend to set scale=0.25
|
|
|
|
self.run()
|
|
|
|
def run(self):
|
|
while True:
|
|
# update the detector with current frame before detection
|
|
self.frame = cv2.cvtColor(self.stream.capture(), cv2.COLOR_RGB2BGR)
|
|
self.detector.update(self.frame)
|
|
|
|
# return a list of coordinates of corresponding target
|
|
enemies = self.detector.detect_enemy()
|
|
if enemies is not None:
|
|
self.plot_points(enemies)
|
|
|
|
items = self.detector.detect_item()
|
|
if items is not None:
|
|
self.plot_points(items)
|
|
|
|
cv2.imshow("frame", self.frame)
|
|
if cv2.waitKey(1) == ord('q'):
|
|
cv2.destroyAllWindows()
|
|
break
|
|
|
|
def plot_points(self, points):
|
|
for point in points:
|
|
cv2.circle(self.frame, (point[0], point[1]), 3, (255, 255, 255), -1)
|