def pipeline():
for img in glob.glob('..\\HemaCam-Data\\Input_Images\\*.jpg'):
image = cv2.imread(img)
imgname, extension = os.path.basename(img).split(".")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
threshold = thresh(image, gray)
cellSegmentation(image, threshold, gray, filepath + imgname)
calcFeatures(image, threshold, gray, filepath2 + imgname, imgname)
def calcHist(roi, filepath, count):
roi_copy = roi.copy()
cellgray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
cellthresh = thresh(roi, cellgray)
cellmask = cellthresh
color = ('b','g','r')
for i,col in enumerate(color):
histr = cv2.calcHist([roi_copy],[i],cellmask,[256],[0,256])
plt.plot(histr,color = col)
plt.xlim([0,256])
fig = plt.gcf()
fig.savefig(rootpath + filepath + "_hist_{}.jpg".format(count))
plt.clf()
for label in np.unique(labels):
if label == 0:
continue
mask = np.zeros(gray.shape, dtype="uint8")
mask[labels == label] = 255
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
c = max(cnts, key=cv2.contourArea)
if c.shape[0] >= 5:
x, y, w, h = cv2.boundingRect(c)
# Filter out small/incomplete cells
area = cv2.contourArea(c)
perimeter = cv2.arcLength(c, True)
if area > 100 and perimeter > 150 and area < 6000 and perimeter < 300:
roi = clean[y:y + h, x:x + w]
cv2.imwrite(rootpath + filepath + "_{}.png".format(count), roi)
# calcHist(roi, filepath, count)
contours.append(c)
count += 1
# print num, count
if __name__ == "__main__":
img, gray = img_load(imgname)
clean = img.copy()
imgthresh = thresh(img, gray)
cellSegmentation(clean, imgthresh, gray)
import cv2
from HemaCamSegmentation import img_load, thresh
from realCellSegmentation import cellSegmentation
from cellArea import cellArea
from boundingEllipse import boundingEllipse
from cellCount import cellCount
from features import calcFeatures
from boundingRectangle import boundingRectangle
imgname = 'Sickle2'
filepath = "demo_out" + "\\" + imgname
rootpath = "C:\\Users\\eshikasaxena\\Desktop\\HemaCam Project\\HemaCam-Data\\"
img = cv2.imread(rootpath + imgname + ".jpg")
cv2.imwrite(rootpath + filepath + ".jpg", img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("img", img)
cv2.imshow("gray", gray)
cv2.imwrite(rootpath + filepath + "_gray.jpg", gray)
threshold = thresh(img, gray)
cv2.imshow("thresh", threshold)
cv2.imwrite(rootpath + filepath + "_thresh.jpg", threshold)
area = cellArea(img, threshold, gray, filepath)
ellipse = boundingEllipse(img, threshold, gray, filepath)
count = cellCount(img, threshold, gray, filepath)
cv2.imwrite(rootpath + filepath + "_count.jpg", count)
rect = boundingRectangle(img, threshold, gray, filepath)
cellSegmentation(img, threshold, gray, filepath)
#calcFeatures(img, threshold, gray, filepath, imgname)
cv2.imshow("out", np.hstack([ellipse, area, rect]))
cv2.waitKey(0)
cv2.destroyAllWindows()