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houghBoxRotate.py
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houghBoxRotate.py
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import numpy as np
import cv2
from PIL import Image, ImageEnhance
import matplotlib.pyplot as plt
import numpy as np
from skimage import io
import os
import PIL
from skimage.filters import threshold_otsu, sobel
from tqdm import tqdm
def houghT_rotate(folder_path, output_folder, outliers_path, th_less):
image_files = os.listdir(folder_path)
non_centered = []
image_output = []
for image in tqdm(image_files, desc="Processing images", unit="image"):
image_path = folder_path + image
im_gray = np.array(Image.open(image_path).convert('L'))
#get rectangle mask
mask, image_masked, no_mask , bg_removed = get_rect(im_gray, th_less) # th_less = percentage to lessen threshold
#use HoughLines now to rotate the rectangle
edges = cv2.Canny(mask, 50, 150, apertureSize=3) #get contours of the rectangle
lines_list =[]
lines = cv2.HoughLinesP(
edges, # Input edge image
1, # Distance resolution in pixels
np.pi/180, # Angle resolution in radians
threshold=100, # Min number of votes for valid line
minLineLength=5, # Min allowed length of line
maxLineGap=5 # Max allowed gap between line for joining them
)
#if no rectangle detected , keep it as is
if lines is None:
centered_image = image_masked
rotated_image = image_masked
rotated_mask = mask
else:
for points in lines:
# Extracted points nested in the list
x1,y1,x2,y2=points[0]
length = np.sqrt((x2 - x1)^2 + (y2 - y1)^2)
lines_list.append(length) #get the length of the line
#get the longest line detected
longest_line = max(lines_list)
longest_line_index = lines_list.index(longest_line)
longest_line = lines[longest_line_index][0] #get the points of the longest line
#get the angle of the longest line
angle_radians = np.arctan2(longest_line[3] - longest_line[1], longest_line[2] - longest_line[0])
angle_degrees = np.degrees(angle_radians)
height, width = im_gray.shape[:2]
center = (width // 2, height // 2)
if no_mask is True:
centered_image = im_gray
elif (angle_degrees <=0.6 and angle_degrees >=0.0):
# print("condition 1")
rotated_image = np.uint8(im_gray)
image_masked = cv2.bitwise_and(rotated_image, rotated_image, mask=mask)
rotated_image = cv2.rotate(image_masked, cv2.ROTATE_90_CLOCKWISE)
rotated_mask = cv2.rotate(mask, cv2.ROTATE_90_CLOCKWISE)
else:
if (angle_degrees >= -90 and angle_degrees <= -70) or (angle_degrees <= 90 and angle_degrees >= 70 ) :
# print("condition 2")
if (angle_degrees <= 90 and angle_degrees >= 70 ):
# print("condition 2.1")
image_masked = cv2.bitwise_and(im_gray, im_gray, mask=mask)
rotated_image = cv2.rotate(np.uint8(image_masked), cv2.ROTATE_90_COUNTERCLOCKWISE)
rotated_mask =cv2.rotate(mask, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif (angle_degrees >= -90 and angle_degrees <= -70):
# print("condition 2.2")
image_masked = cv2.bitwise_and(im_gray, im_gray, mask=mask)
rotated_image = cv2.rotate(np.uint8(image_masked), cv2.ROTATE_90_CLOCKWISE)
rotated_mask =cv2.rotate(mask, cv2.ROTATE_90_CLOCKWISE)
else:
#where mask is horizontal but base image is vertical crops image inside
# print("condition 3")
rotation_matrix = cv2.getRotationMatrix2D(center, angle_degrees, 1) #1 is image zoo,
#making sure the height is always longer. so image is always vertical
if height < width:
# print("condition 3.1")
image_masked = cv2.bitwise_and(im_gray, im_gray, mask=mask)
rotated_image = cv2.warpAffine(image_masked, rotation_matrix, (width, height))
rotated_mask = cv2.warpAffine(mask, rotation_matrix, (width, height))
else:
# print("condition 3.2")
# im_gray = np.uint8(img_original)
image_masked = cv2.bitwise_and(im_gray, im_gray, mask=mask)
rotated_image = cv2.warpAffine(image_masked, rotation_matrix, (height, width))
rotated_mask = cv2.warpAffine(mask, rotation_matrix, (height, width))
#rotation of whole image (not just the mask)
if angle_degrees < -0.6:
# print("condition 4")
rotated_image = cv2.rotate(rotated_image, cv2.ROTATE_90_COUNTERCLOCKWISE)
rotated_mask = cv2.rotate(rotated_mask, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif angle_degrees > 0.6:
# print("condition 5")
rotated_image = cv2.rotate(rotated_image, cv2.ROTATE_90_CLOCKWISE)
rotated_mask = cv2.rotate(rotated_mask, cv2.ROTATE_90_CLOCKWISE)
# if there's no mask/rectangle detected will not auto center image
# mask is the basis for the centering
if no_mask == True:
centered_image = im_gray
else:
centered_image = center_object(rotated_image , rotated_mask)
image_output.append(centered_image)
non_centered.append(rotated_image) #for double checking
cv2.imwrite(output_folder+image, centered_image)
return image_output , non_centered
def get_rect(im_gray, th_less):
#get image threshold
threshold = threshold_otsu(im_gray)
threshold -= threshold * th_less #lessen threshold
bina_image = im_gray < threshold
inverted_bina_image = np.logical_not(bina_image)
#removed image background after thresholding (not perfect)
background_removed_image = np.zeros_like(im_gray)
background_removed_image[inverted_bina_image] = im_gray[inverted_bina_image]
background_removed_image = Image.fromarray(background_removed_image)
# #brighten image on ght first run only. will help with detecting a better rectangle
# if i == 0:
# enhancer = ImageEnhance.Brightness(background_removed_image)
# factor = 1.5
# background_removed_image = enhancer.enhance(factor)
#get contours of binary image
contours, _ = cv2.findContours(np.uint8(background_removed_image), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#find the best rectangle that will fit to the humerus
best_rect = None
best_rect_area = 0
for contour in contours:
rect = cv2.minAreaRect(contour)
box = cv2.boxPoints(rect)
box_area = rect[1][0] * rect[1][1]
if box_area > best_rect_area:
best_rect = box
best_rect_area = box_area
#draw the rectangle on the original image. then make the inside white
#now we have a white rectangle
original_image = np.array(background_removed_image)
mask = np.zeros_like(original_image)
no_mask = False
cv2.drawContours(mask, [np.int0(best_rect)], 0, (255, 255, 255), cv2.FILLED) # get the mask rectangle
image_masked = cv2.bitwise_and(original_image, original_image, mask=mask) #getting only object insde the rectangle
#sometimes it cannot detect a rectangle. so we use the original image
if all(element == 255 for row in mask for element in row): # to know if there was no mask detected or not
#if no mask, will not center image
no_mask = True
return mask, image_masked, no_mask, background_removed_image
def center_object(rotated_image, rotated_mask):
w, h, x, y=0,0,0,0
hh, ww = rotated_mask.shape
# get the contours of the rotated image
contours = cv2.findContours(rotated_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
x,y,w,h = cv2.boundingRect(cntr)
# recenter
startx = (ww - w)//2
starty = (hh - h)//2
result = np.zeros_like(rotated_image)
result[starty:starty+h,startx:startx+w] = rotated_image[y:y+h,x:x+w]
return result
# def to_invert_image(image):
# # Define the number of levels for quantization
# num_levels = 4 # Adjust this as needed
# # Calculate the interval between levels
# interval = 255 / (num_levels - 1)
# # Apply quantization by rounding pixel values to the nearest level
# quantized_image = np.round(image / interval) * interval
# # Convert the pixel values back to uint8 type
# quantized_image = quantized_image.astype(np.uint8)
# # Check if black or white is closer to the mean of the pixel values
# if np.abs(quantized_image.mean()) > np.abs(quantized_image.mean() - 255):
# image = cv2.bitwise_not(image)
# return image
#sources:
# image center: https://stackoverflow.com/questions/59525640/how-to-center-the-content-object-of-a-binary-image-in-python
# hough transform: https://medium.com/wearesinch/correcting-image-rotation-with-hough-transform-e902a22ad988