42 lines
1.5 KiB (Stored with Git LFS)
Python
42 lines
1.5 KiB (Stored with Git LFS)
Python
import cv2
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from cv2.typing import MatLike
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import numpy as np
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POOL_SIZES = 10
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POOL_KERNAL = np.ones((POOL_SIZES,POOL_SIZES),np.float32)/(POOL_SIZES*POOL_SIZES)
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WINDOW_WIDTH = 21
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def process_frame(frame: MatLike, thresh: float) -> tuple[MatLike, MatLike, list[tuple[int, int]], list[tuple[int, int]], list[tuple[int, int]]]:
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frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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frame_hls = cv2.cvtColor(frame, cv2.COLOR_BGR2HLS)
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H_filter_mask = cv2.inRange(frame_hls[:,:,0], 100, 140)
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L_filter_mask = cv2.inRange(frame_hls[:,:,1], 20, 210)
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S_filter_mask = cv2.inRange(frame_hls[:,:,2], 200, 255)
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mask = cv2.bitwise_and(S_filter_mask, H_filter_mask)
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mask = cv2.bitwise_and(mask, L_filter_mask)
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frame_grey_filtered = cv2.bitwise_and(frame_gray, mask)
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frame_convolution_mask = cv2.inRange(cv2.filter2D(frame_grey_filtered, -1, POOL_KERNAL), 1, 255)
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WIDTH = frame_convolution_mask.shape[1]
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HEIGHT = frame_convolution_mask.shape[0]
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pos_windows = []
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neg_windows = []
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neutral_windows = []
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for x in range(0, WIDTH - WINDOW_WIDTH, 5):
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for y in range(0, HEIGHT - WINDOW_WIDTH, 5):
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score = np.sum(frame_convolution_mask[y:y+WINDOW_WIDTH, x:x+WINDOW_WIDTH])
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if score > 255*(WINDOW_WIDTH*WINDOW_WIDTH*thresh):
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pos_windows += [(x, y)]
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elif score < 1:
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neg_windows += [(x, y)]
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else:
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neutral_windows += [(x, y)]
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return frame_gray, frame_convolution_mask, pos_windows, neg_windows, neutral_windows
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