def getthresholdedimg(im):
imghsv = cv.CreateImage(cv.GetSize(im), 8, 3)
# Convert image from RGB to HSV
cv.CvtColor(im, imghsv, cv.CV_BGR2HSV)
imgthreshold = cv.CreateImage(cv.GetSize(im), 8, 1)
cv.InRangeS(imghsv, cv.Scalar(23, 100, 100), cv.Scalar(25, 255, 255),
imgthreshold) # catch the orange yellow blob
return imgthreshold
def earthmoverdistance(hist1, hist2, h_bins, s_bins):
#Define number of rows
numRows = h_bins * s_bins
sig1 = np.array((numRows, 3), dtype=float)
sig2 = np.array((numRows, 3), dtype=float)
for h in range(h_bins):
for s in range(s_bins):
bin_val = cv2.QueryHistValue_2D(hist1, h, s)
cv2.Set2D(sig1, h * s_bins + s, 0, cv2.Scalar(bin_val))
cv2.Set2D(sig1, h * s_bins + s, 1, cv2.Scalar(h))
cv2.Set2D(sig1, h * s_bins + s, 2, cv2.Scalar(s))
bin_val = cv2.QueryHistValue_2D(hist2, h, s)
cv2.Set2D(sig2, h * s_bins + s, 0, cv2.Scalar(bin_val))
cv2.Set2D(sig2, h * s_bins + s, 1, cv2.Scalar(h))
cv2.Set2D(sig2, h * s_bins + s, 2, cv2.Scalar(s))
#This is the important line were the OpenCV EM algorithm is called
return cv2.CalcEMD2(sig1, sig2, cv2.CV_DIST_L2)
import numpy as np
import cv2
lower_color_bounds = cv2.Scalar(100, 0, 0)
upper_color_bounds = cv2.Scalar(225, 80, 80)
frame = cv2.imread('octo_first_layer.PNG', 1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mask = cv2.inRange(frame, lower_color_bounds, upper_color_bounds)
mask_rgb = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
frame = frame & mask_rgb
cv2.imshow('Frame', frame)
cv2.waitKey(0)
def main():
inputFile, outputFile = (sys.argv[1], sys.argv[2])
im = cv2.imread(inputFile)
cv2.Scalar(200, 200, 200)
wim = cv2.inRange(im, np.array([200, 200, 200]), np.array([255, 255, 255]))
cv2.imwrite(filename=outputFile, img=wim)
if __name__ == "__main__":
orig = cv.LoadImage(
r"C:\git\Python-Snippets\Image Recognition\images\D2C-Logins - RunDate 2019-10-03 - Part (22).image"
)
#Convert in black and white
res = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.CvtColor(orig, res, cv.CV_BGR2GRAY)
#Operations on the image
openCloseImage(res)
dilateImage(res, 2)
erodeImage(res, 2)
smoothImage(res, 5)
thresholdImage(res, 150, cv.CV_THRESH_BINARY_INV)
#Get contours approximated
contourLow = getContours(res, 3)
#Draw them on an empty image
final = cv.CreateImage(cv.GetSize(res), 8, 1)
cv.Zero(final)
cv.DrawContours(final, contourLow, cv.Scalar(255), cv.Scalar(255), 2,
cv.CV_FILLED)
cv.ShowImage("orig", orig)
cv.ShowImage("image", res)
cv.SaveImage("modified.png", res)
cv.ShowImage("contour", final)
cv.SaveImage("contour.png", final)
cv.WaitKey(0)
import glob
import os
import cv2
if not os.path.exists('label'):
os.mkdir('label')
white_low = cv2.Scalar(0,0,0)
white_high = cv2.Scalar(0,0,255)
yellow_low = cv2.Scalar(20,100,100)
yellow_high = cv2.Scalar(30,255,255)
path = "data\\*.jpg"
for file in glob.glob(path):
raw = cv2.imread(file)
hsv = cv2.cvtColor(raw, cv2.COLOR_BGR2HSV)
white_mask = cv2.inRange(hsv, white_low, white_high)
yellow_mask = cv2.inRange(hsv, yellow_low, yellow_high)
mask = white_mask + yellow_mask
filename = os.path.splittext(file)[0]
cv2.imwrite("label\\"+filename+".png", 255*mask)