mirror of
https://github.com/SilverComet7/yolov5-DNF.git
synced 2026-05-06 22:08:06 +08:00
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
import cv2 as cv
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def score(img):
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counter = 0
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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if img[i,j] > 127:
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counter += 1
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return counter/(img.shape[0] * img.shape[1])
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def img_show(img):
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cv.imshow("win", img)
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cv.waitKey(0)
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cv.destroyAllWindows()
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skill_height = int((793-733)/2)
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skill_width = int((750-538)/7)
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dict = {"A": (733+skill_height, 538), "S": (733+skill_height, 538+skill_width), "D": (733+skill_height, 538+2*skill_width),
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"F": (733+skill_height, 538+3*skill_width), "G": (733+skill_height, 538+4*skill_width),
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"H": (733+skill_height, 538+5*skill_width), "Q": (733, 538), "W": (733, 538+skill_width), "E": (733, 538+2*skill_width),
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"R": (733, 538+3*skill_width), "T": (733, 538+4*skill_width), "Y": (733, 538+5*skill_width)}
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def skill_rec(skill_name, img):
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if skill_name == "X":
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return True
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skill_img = img[dict[skill_name][0]: dict[skill_name][0]+skill_height,
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dict[skill_name][1]: dict[skill_name][1]+skill_width, 2]
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if score(skill_img) > 0.1:
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return True
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else:
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return False
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if __name__ == "__main__":
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img_path = "datasets/guiqi/test/20_93.jpg"
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img = cv.imread(img_path)
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print(skill_height, skill_width)
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print(img.shape)
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skill_img = img[733: 793, 538:750, 2]
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img_show(skill_img)
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skill_imgA = img[dict["A"][0]: dict["A"][0]+skill_height, dict["A"][1]: dict["A"][1]+skill_width, 2]
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skill_imgH= img[dict["H"][0]: dict["H"][0]+skill_height, dict["H"][1]: dict["H"][1]+skill_width, 2]
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skill_imgG= img[dict["G"][0]: dict["G"][0]+skill_height, dict["G"][1]: dict["G"][1]+skill_width, 2]
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skill_imgE= img[dict["E"][0]: dict["E"][0]+skill_height, dict["E"][1]: dict["E"][1]+skill_width, 2]
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skill_imgQ= img[dict["Q"][0]: dict["Q"][0]+skill_height, dict["Q"][1]: dict["Q"][1]+skill_width, 2]
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skill_imgS= img[dict["S"][0]: dict["S"][0]+skill_height, dict["S"][1]: dict["S"][1]+skill_width, 2]
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skill_imgY= img[dict["Y"][0]: dict["Y"][0]+skill_height, dict["Y"][1]: dict["Y"][1]+skill_width, 2]
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skill_imgD = img[dict["D"][0]: dict["D"][0]+skill_height, dict["D"][1]: dict["D"][1]+skill_width, 2]
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skill_imgF = img[dict["F"][0]: dict["F"][0]+skill_height, dict["F"][1]: dict["F"][1]+skill_width, 2]
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skill_imgW = img[dict["W"][0]: dict["W"][0]+skill_height, dict["W"][1]: dict["W"][1]+skill_width, 2]
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skill_imgR = img[dict["R"][0]: dict["R"][0]+skill_height, dict["R"][1]: dict["R"][1]+skill_width, 2]
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# print("A", np.mean(skill_imgA))
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# print("H", np.mean(skill_imgH))
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# print("G", np.mean(skill_imgG))
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# print("E", np.mean(skill_imgE))
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# print("Q", np.mean(skill_imgQ))
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# print("S", np.mean(skill_imgS))
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# print("Y", np.mean(skill_imgY))
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print("A", score(skill_imgA))
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print("Q", score(skill_imgQ))
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print("S", score(skill_imgS))
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print("D", score(skill_imgD))
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print("F", score(skill_imgF))
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print("W", score(skill_imgW))
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print("R", score(skill_imgR))
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print("Y", score(skill_imgY))
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print("H", score(skill_imgH))
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print("G", score(skill_imgG))
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print("E", score(skill_imgE))
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print(skill_rec("W", img))
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