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)
if c.shape[0] >= 5:
x, y, w, h = cv2.boundingRect(c)
area = cv2.contourArea(c)
perimeter = cv2.arcLength(c, True)
if area > 100 and perimeter > 150 and area < 6000 and perimeter < 300:
features = extractFeatures(c)
# Original
# features = [imgname + "_{}".format(count) + ".png"] + [count] + features
# MODIFIED
# features = [imgname + "_{}".format(count) + ".png"] + [ '<img src=' + '"..\\Cell_Images\\' + imgname + "_{}".format(count) + ".png" + '">'] + [count] + features
features = [imgname + "_{}".format(count) + ".png"] + [
'<img src="' + imgname + "_{}".format(count) + ".png" +
'">'
] + features
# End Modified
append_csv(features, filepath + "_data")
roi = clean[y:y + h, x:x + w]
contours.append(c)
count += 1
# print num, count
if __name__ == "__main__":
img, gray = img_load("0073")
clean = img.copy()
imgthresh = thresh(img, gray)
calcFeatures(clean, imgthresh, gray)