import matplotlib.pyplot as pltimport matplotlib.image as mpimgimport numpy as np # Read in the imageimage = mpimg.imread('test.jpg') # Grab the x and y sizes and make two copies of the image# With one copy we'll extract only the pixels that meet our selection,# then we'll paint those pixels red in the original image to see our selection# overlaid on the original.ysize = image.shape[0]xsize = image.shape[1]color_select= np.copy(image)line_image = np.copy(image) # Define our color criteriared_threshold = 220green_threshold = 220blue_threshold = 220rgb_threshold = [red_threshold, green_threshold, blue_threshold] # Define a triangle region of interest (Note: if you run this code,left_bottom = [0, ysize-1]right_bottom = [xsize-1, ysize-1]apex = [650, 400] fit_left = np.polyfit((left_bottom[0], apex[0]), (left_bottom[1], apex[1]), 1)fit_right = np.polyfit((right_bottom[0], apex[0]), (right_bottom[1], apex[1]), 1)fit_bottom = np.polyfit((left_bottom[0], right_bottom[0]), (left_bottom[1], right_bottom[1]), 1) # Mask pixels below the thresholdcolor_thresholds = (image[:,:,0] < rgb_threshold[0]) | \ (image[:,:,1] < rgb_threshold[1]) | \ (image[:,:,2] < rgb_threshold[2]) # Find the region inside the linesXX, YY = np.meshgrid(np.arange(0, xsize), np.arange(0, ysize))region_thresholds = (YY > (XX*fit_left[0] + fit_left[1])) & \ (YY > (XX*fit_right[0] + fit_right[1])) & \ (YY < (XX*fit_bottom[0] + fit_bottom[1]))# Mask color selectioncolor_select[color_thresholds] = [0,0,0]# Find where image is both colored right and in the regionline_image[~color_thresholds & region_thresholds] = [255,0,0] # Display our two output imagesplt.imshow(color_select)plt.imshow(line_image) # uncomment if plot does not displayplt.show()
import matplotlib.pyplot as pltimport matplotlib.image as mpimgimport numpy as npimport cv2 # Read in and grayscale the imageimage = mpimg.imread('test.jpg')gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY) # Define a kernel size and apply Gaussian smoothingkernel_size = 5blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) # Define our parameters for Canny and applylow_threshold = 50high_threshold = 150edges = cv2.Canny(blur_gray, low_threshold, high_threshold) # Next we'll create a masked edges image using cv2.fillPoly()mask = np.zeros_like(edges)ignore_mask_color = 255 # This time we are defining a four sided polygon to maskimshape = image.shapevertices = np.array([[(0,imshape[0]),(0, 0), (imshape[1], 0), (imshape[1],imshape[0])]], dtype=np.int32) # all image# vertices = np.array([[(0,imshape[0]),(554, 460), (700, 446), (imshape[1],imshape[0])]], dtype=np.int32) # defining a quadrilateral regioncv2.fillPoly(mask, vertices, ignore_mask_color)masked_edges = cv2.bitwise_and(edges, mask) # Define the Hough transform parameters# Make a blank the same size as our image to draw onrho = 1 # distance resolution in pixels of the Hough gridtheta = np.pi/180 # angular resolution in radians of the Hough gridthreshold = 1 # minimum number of votes (intersections in Hough grid cell)min_line_length = 5 #minimum number of pixels making up a linemax_line_gap = 1 # maximum gap in pixels between connectable line segmentsline_image = np.copy(image)*0 # creating a blank to draw lines on # Run Hough on edge detected image# Output "lines" is an array containing endpoints of detected line segmentslines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap) # Iterate over the output "lines" and draw lines on a blank imagefor line in lines: for x1,y1,x2,y2 in line: cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10) # Create a "color" binary image to combine with line imagecolor_edges = np.dstack((edges, edges, edges)) # Draw the lines on the edge imagelines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0)plt.imshow(lines_edges)plt.show()
