def startFrame():
# Read a frame from feed
global frame
ret, frame = cap.read()
# Convert to greyscale frame
global frame_gray
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Get histogram of frame
hist_img = cv2.calcHist([frame], [0], None, [256], [0, 256])
#plt.plot(hist_img)
#plt.show()
# Pass frame to histogram adjustment to remove ouliers
hist_no_outliers, lower_index = outliers.removeOutliersThresh(hist_img)
#plt.plot(hist_no_outliers)
#plt.show()
# Pass histogram to adaptive thresholding to determine level
global threshLevel
threshLevel = thresh.bi_level_img_threshold(hist_no_outliers)
# Adjust start index of hist and add manual level adjustment
global threshLevelAdjust
threshLevelAdjust = threshLevel + lower_index + 15
#print('Bi level thresh', threshLevelAdjust)
struct_el = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(25,25))
global frame_open
frame_open = cv2.morphologyEx(frame_gray, cv2.MORPH_OPEN, struct_el)
#cv2.imshow('open',frame_open)
return frame_open
#while(cap.isOpened()):
# Read a frame from feed
# ret, frame = cap.read()
# Convert to greyscale frame
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
i = i+1
print('i ', i)
# Get histogram of frame
hist_img = cv2.calcHist([frame_gray], [0], None, [256], [0, 256])
#plt.plot(hist_img)
#plt.show()
# Pass frame to histogram adjustment to remove ouliers
hist_no_outliers, lower_index = outliers.removeOutliersThresh(hist_img)
#plt.plot(hist_no_outliers)
#plt.show()
# Pass histogram to adaptive thresholding to determine level
threshLevel = thresh.bi_level_img_threshold(hist_no_outliers)
# Adjust start index of hist and add manual level adjustment
threshLevelAdjust = 40 #threshLevel + lower_index
print('Bi level thresh', threshLevelAdjust)
plt.plot(hist_no_outliers)
plt.show()
struct_el = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(25,25))
frame_open = cv2.morphologyEx(frame_gray, cv2.MORPH_OPEN, struct_el)
