Commit 0b13d313 authored by fouad5's avatar fouad5
Browse files

feature detection using lineair classification

parent 79d580bb
import numpy as np
import cv2
import argparse
from detect_shapes import detecting
def getSobel (channel):
......@@ -36,96 +34,14 @@ def findSignificantContours (img, sobel_8u):
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 getCroppedImage(image):
# im1 = cv2.imread(image)
#image1 = imread("/path/to/image1")
#image2 = imread("/path/to/image2")
# image3 = im1 - im
img = image #cv2.imread(image)
# Crop from x, y, w, h -> 100, 200, 300, 400
# NOTE: its img[y: y + h, x: x + w] and *not* img[x: x + w, y: y + h]
crop_img = img[300:550, 300:550]
#cv2.imshow("cropped", crop_img)
cv2.imwrite("result.jpg", crop_img)
#cv2.waitKey(0)
# cv2.imshow('frame',img)
# cv2.waitKey(0)
return crop_img
# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
# fgbg = cv2.createBackgroundSubtractorGMG()
# fgmask = fgbg.apply(im1)
# fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)
# cv2.imshow('frame',fgmask)
# cv2.imshow("Image", img)
# cv2.waitKey(0)
# imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
# ret,thresh = cv2.threshold(imgray,127,255,0)
# contours, hierarchy, _ = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# ctr = np.array(contours).reshape((-1,1,2)).astype(np.int32)
# cv2.drawContours(im1, [ctr], 0, (0,255,0), -1)
# cv2.imwrite("result.jpg", im1)
# cv2.waitKey(0)
def getNegative(img):
background = cv2.imread('background.jpg')
background = background[300:550, 300:550]
img = cv2.absdiff(background,img)
return img
def getFeatures (image):
size = shape = color = ""
copy = image
img = getCroppedImage(image)
img = getNegative(img)
# find object
detection = detecting(img)
def segment (img):
shape = detection[0]
if(detection[2] > 7000):
size = "BIG"
elif(detection[2] > 5000):
size = "MEDIUM"
else:
size = "SMALL"
img = getCroppedImage(copy)
# img = cv2.imread(path)
# getObjectFromImage('background.jpg', 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 )
# Noise reduction trick, from http://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l182
mean = np.median(sobel)
# Zero any values less than mean. This reduces a lot of noise.
......@@ -138,77 +54,22 @@ def getFeatures (image):
# Find contours
significant = findSignificantContours(img, sobel_8u)
# print("significant: ", significant)
# print("cnts: ", [shape[1]])
# Mask
mask = sobel.copy()
mask[mask > 0] = 0
cv2.fillPoly(mask, [detection[1]], 255)
cv2.fillPoly(mask, significant, 255)
# Invert mask
mask = np.logical_not(mask)
#Finally remove the background
img[mask] = 0;
return img
# fname = path.split('/')[-1]
# cv2.imwrite('output/' + fname, img);
#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
# BGR
boundaries = [
([0, 0, 140], [140, 140, 255]), #red
([140, 0, 0], [255, 140, 140]), #blue
([0, 140, 0], [140, 255, 140]), #green
([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)
color = getColor(colorValues, ['RED', 'BLUE', 'GREEN', 'GREY'], max(colorValues))
return {'shape': shape, 'color': color, 'size': size}
image = cv2.imread('sphere3.jpg')
print(getFeatures(image))
# cv2.imshow('output', img)
# cv2.key
# cv2.imwrite('output/' + fname, img)
# print (path)
#segment('original-small.jpg')
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