comment code for plotting image (e7c97bed) · Commits · Sparkf / SUSTech EE326 Digital image Processing Project

train1/ld_module_binary.py

+7 −0

Original line number	Diff line number	Diff line
		@@ -2,6 +2,7 @@ import numpy as np
		import cv2
		import ld_module_transform
		from numba import njit, prange
		import matplotlib.pyplot as plt

		def get_bin_img(img, kernel_size=3, sobel_dirn='X', sobel_thresh=(0, 255), r_thresh=(0, 255),
		s_thresh=(0, 255), b_thresh=(0, 255), g_thresh=(0, 255)):
		@@ -29,6 +30,7 @@ def get_bin_img(img, kernel_size=3, sobel_dirn='X', sobel_thresh=(0, 255), r_thr
		# Threshold R color channel
		r_binary = get_rgb_thresh_img(img, thresh=r_thresh)


		# Threshhold G color channel
		g_binary = get_rgb_thresh_img(img, thresh=g_thresh, channel='G')

		@@ -38,10 +40,12 @@ def get_bin_img(img, kernel_size=3, sobel_dirn='X', sobel_thresh=(0, 255), r_thr
		# Threshold color channel
		s_binary = get_hls_sthresh_img(img, thresh=s_thresh)


		# If two of the three are activated, activate in the binary image
		combined_binary = np.zeros_like(combined)
		combined_binary[(r_binary == 1) \| (combined == 1) \| (s_binary == 1) \| (b_binary == 1) \| (g_binary == 1)] = 1


		return combined_binary


		@@ -52,6 +56,7 @@ def get_hls_sthresh_img(img, thresh=(0, 255)):
		binary_output = np.zeros_like(S).astype(np.uint8)
		binary_output[(S >= thresh[0]) & (S < thresh[1])] = 1


		return binary_output


		@@ -62,6 +67,7 @@ def get_lab_bthresh_img(img, thresh=(0, 255)):
		bin_op = np.zeros_like(B).astype(np.uint8)
		bin_op[(B >= thresh[0]) & (B < thresh[1])] = 1


		return bin_op


		@@ -102,4 +108,5 @@ def abs_thresh(img, sobel_kernel=9, mag_thresh=(0, 255), return_grad=False, dire
		grad_binary = np.zeros_like(scaled_sobel)
		grad_binary[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel < mag_thresh[1])] = 1


		return grad_binary

train1/main.py

+4 −5

Original line number	Diff line number	Diff line
		@@ -91,7 +91,7 @@ def lane_detection(input_img, original_img, i):
		# plt.subplot(121)

		# 3.1.3 find lane via sliding window
		plt.imshow(input_img)
		# plt.imshow(input_img)
		mark_layer = np.zeros((600, 600, 3), np.uint8)
		for midpoint in img_peak_histogram:
		# midpoint (width)
		@@ -134,8 +134,8 @@ def lane_detection(input_img, original_img, i):
		# simulate not save image
		cv2.imwrite("op_module/" + foldername + "_" + filename + ".png", original_img)

		plt.imshow(cv2.cvtColor(original_img.astype("uint8"), cv2.COLOR_BGR2RGB))
		plt.savefig("output.svg", format='svg', dpi=600)
		# plt.imshow(cv2.cvtColor(original_img.astype("uint8"), cv2.COLOR_BGR2RGB))
		# plt.savefig("output.svg", format='svg', dpi=600)


		for subdir, dirs, files in os.walk(rootdir):
		@@ -159,7 +159,7 @@ for subdir, dirs, files in os.walk(rootdir):
		input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)

		# yellow filter
		input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
		# input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
		original_img = input_img
		# img_buffer_array[rotate_num] = input_img
		# input_img = (img_buffer_array[(rotate_num) % 4] * 0.25 + img_buffer_array[(rotate_num - 1) % 4] * 0.25 +
		@@ -177,4 +177,3 @@ for subdir, dirs, files in os.walk(rootdir):

		lane_detection(input_img, original_img, k2)

		print("--- %s seconds ---" % (time.time() - start_time))

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