Commit 4301adb9 authored by fouad5's avatar fouad5
Browse files

feature-detection: inital color detection

parent 95d78dce
import numpy as np
import cv2
import argparse
def getSobel (channel):
sobelx = cv2.Sobel(channel, cv2.CV_16S, 1, 0, borderType=cv2.BORDER_REPLICATE)
sobely = cv2.Sobel(channel, cv2.CV_16S, 0, 1, borderType=cv2.BORDER_REPLICATE)
sobel = np.hypot(sobelx, sobely)
return sobel;
def findSignificantContours (img, sobel_8u):
image, contours, heirarchy = cv2.findContours(sobel_8u, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Find level 1 contours
level1 = []
for i, tupl in enumerate(heirarchy[0]):
# Each array is in format (Next, Prev, First child, Parent)
# Filter the ones without parent
if tupl[3] == -1:
tupl = np.insert(tupl, 0, [i])
level1.append(tupl)
# From among them, find the contours with large surface area.
significant = []
tooSmall = sobel_8u.size * 5 / 100 # If contour isn't covering 5% of total area of image then it probably is too small
for tupl in level1:
contour = contours[tupl[0]];
area = cv2.contourArea(contour)
if area > tooSmall:
cv2.drawContours(img, [contour], 0, (0,255,0),2, cv2.LINE_AA, maxLevel=1)
significant.append([contour, area])
significant.sort(key=lambda x: x[1])
return [x[0] for x in significant];
def getColor(colorValues, colorNames, maxValue):
for i, item in enumerate(colorValues):
if(item == maxValue):
return colorNames[i]
def segment (path):
img = cv2.imread(path)
blurred = cv2.GaussianBlur(img, (5, 5), 0) # Remove noise
# Edge operator
sobel = np.max( np.array([ getSobel(blurred[:,:, 0]), getSobel(blurred[:,:, 1]), getSobel(blurred[:,:, 2]) ]), axis=0 )
mean = np.median(sobel)
# Zero any values less than mean. This reduces a lot of noise.
sobel[sobel <= mean] = 0;
sobel[sobel > 255] = 255;
cv2.imwrite('output/edge.png', sobel);
sobel_8u = np.asarray(sobel, np.uint8)
# Find contours
significant = findSignificantContours(img, sobel_8u)
# Mask
mask = sobel.copy()
mask[mask > 0] = 0
cv2.fillPoly(mask, significant, 255)
# Invert mask
mask = np.logical_not(mask)
#Finally remove the background
img[mask] = 0;
fname = path.split('/')[-1]
cv2.imwrite('output/' + fname, img);
print (path)
#cv2.imshow("Image", img)
#cv2.waitKey(0)
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", help = "path to the image")
args = vars(ap.parse_args())
# load the image
image = img # cv2.imread(args["image"])
# define the list of boundaries
boundaries = [
([17, 15, 100], [255, 56, 200]), #red
([86, 31, 4], [220, 88, 50]), #blue
([25, 146, 190], [62, 174, 250]), #yellowish
([103, 86, 65], [145, 133, 128]) #grey
]
colorValues = []
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype = "uint8")
upper = np.array(upper, dtype = "uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
#print(cl.label(image, mask))
# save the values
colorValues.append(np.count_nonzero(mask))
#print(np.count_nonzero(mask))
output = cv2.bitwise_and(image, image, mask = mask)
# show the images
#cv2.imshow("images", np.hstack([image, output]))
#cv2.waitKey(0)
print(getColor(colorValues, ['RED', 'BLUE', 'YELLOW(ish)', 'GREY'], max(colorValues)))
segment('bal1.jpg')
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