Cropping in OpenCV return images rotated 90 degrees
Question:
I want to crop images according to their right frame. I have about 10000 of hand X-ray images to preprocess, and what I have done so far:
- Apply Gaussian Blur and Threshold (Binary + Otsu) on the image.
- Apply dilation to get a single object (in this case a hand).
- Used
cv2.findContours()
to draw outline along the edges around the hand.
- Used
cv2.boundingRect()
to find the right frame, and then cv2.minAreaRect()
and cv2.boxPoints
to get the right points for the bounding box.
- Used
cv2.warpPerspective
to adjust image according to height and width.
The code below describes the above:
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image, create mask, grayscale, Gaussian blur, Otsu's threshold
img_path = "sample_image.png"
image = cv2.imread(image_path)
original = image.copy()
blank = np.zeros(image.shape[:2], dtype = np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (33,33), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Merge text into a single contour
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations = 3)
# Find contours
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key = lambda x: cv2.boundingRect(x)[0])
for c in cnts:
# Filter using contour area and aspect ratio (x1 = width, y1 = height)
x, y, x1, y1 = cv2.boundingRect(c)
if (x1 > 500) and (y1 > 700):
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)
width = int(rect[1][0])
height = int(rect[1][1])
src_pts = box.astype("float32")
dst_pts = np.array([[0, height-1], [0, 0],
[width-1, 0], [width-1, height-1]], dtype="float32")
M = cv2.getPerspectiveTransform(src_pts, dst_pts)
warped = cv2.warpPerspective(image, M, (width, height))
plt.imshow(warped)
If you have a look at some of the images in the folder, those are the inputs. When I run these images through the code above, I get an output like this. Some of them are cropped nicely (straightened), however, some of them are cropped with 90 degree rotations. Is there a code to counter the ‘rotating 90 degrees output’ problem?
Here are some images:
Image Inputs: Four X-ray examples
Image Outputs: Returns images that are 90 degrees rotated
Image Outputs wanted: Straightened image (Just used Photoshop to straighten them. Dont want to do this for 10000 images…)
UPDATE:
I edited the code according to below-mentioned suggestions. After running the some samples, it now returns images that are now 90 degrees slanted to the right.
I doubt it’s because of the quality of the images. Maybe it’s got to do with OpenCV’s minAreaRect()
? or boxPoints
?
FINAL UPDATE:
According to @Prashant Maurya, the code was updated with a function added to detect whether the position of the hand is left or right. And then mapping src_pts to right dst_pts. Full code is shown below.
Answers:
Hi there are two changes which will correct the output:
- The width and height taken in the code is in the wrong order ie: width: 1470 & height: 1118 just switch the values:
- Map src_pts to right dst_pts the current code is mapping top left
corner to bottom left therefore the image is being rotated.
- Added function to detect whether image is right tilted or left and rotate and rotate it accordingly
Full code with changes is:
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image, create mask, grayscale, Gaussian blur, Otsu's threshold
img_path = "xray1.png"
image = cv2.imread(img_path)
cv2.imshow("image original", image)
cv2.waitKey(10000)
original = image.copy()
blank = np.zeros(image.shape[:2], dtype = np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (33,33), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Merge text into a single contour
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations = 3)
# Find contours
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key = lambda x: cv2.boundingRect(x)[0])
def get_tilt(box):
tilt = "Left"
x_list = [coord[0] for coord in box]
y_list = [coord[1] for coord in box]
print(x_list)
print(y_list)
x_list = sorted(x_list)
y_list = sorted(y_list)
print(x_list)
print(y_list)
for coord in box:
if coord[0] == x_list[0]:
index = y_list.index(coord[1])
print("Index: ", index)
if index == 1:
tilt = "Left"
else:
tilt = "Right"
return tilt
for c in cnts:
# Filter using contour area and aspect ratio (x1 = width, y1 = height)
x, y, x1, y1 = cv2.boundingRect(c)
if (x1 > 500) and (y1 > 700):
rect = cv2.minAreaRect(c)
print("rect",rect)
box = cv2.boxPoints(rect)
box = np.int0(box)
# print("rect:", box)
tilt = get_tilt(box)
src_pts = box.astype("float32")
if tilt == "Left":
width = int(rect[1][1])
height = int(rect[1][0])
dst_pts = np.array([[0, 0],
[width-1, 0], [width-1, height-1], [0, height-1]], dtype="float32")
else:
width = int(rect[1][0])
height = int(rect[1][1])
dst_pts = np.array([[0, height-1], [0, 0],
[width-1, 0], [width-1, height-1]], dtype="float32")
print("Src pts:", src_pts)
print("Dst pts:", dst_pts)
M = cv2.getPerspectiveTransform(src_pts, dst_pts)
warped = cv2.warpPerspective(image, M, (width, height))
print("Showing image ..")
# plt.imshow(warped)
cv2.imshow("image crop", warped)
cv2.waitKey(10000)
I want to crop images according to their right frame. I have about 10000 of hand X-ray images to preprocess, and what I have done so far:
- Apply Gaussian Blur and Threshold (Binary + Otsu) on the image.
- Apply dilation to get a single object (in this case a hand).
- Used
cv2.findContours()
to draw outline along the edges around the hand. - Used
cv2.boundingRect()
to find the right frame, and thencv2.minAreaRect()
andcv2.boxPoints
to get the right points for the bounding box. - Used
cv2.warpPerspective
to adjust image according to height and width.
The code below describes the above:
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image, create mask, grayscale, Gaussian blur, Otsu's threshold
img_path = "sample_image.png"
image = cv2.imread(image_path)
original = image.copy()
blank = np.zeros(image.shape[:2], dtype = np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (33,33), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Merge text into a single contour
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations = 3)
# Find contours
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key = lambda x: cv2.boundingRect(x)[0])
for c in cnts:
# Filter using contour area and aspect ratio (x1 = width, y1 = height)
x, y, x1, y1 = cv2.boundingRect(c)
if (x1 > 500) and (y1 > 700):
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)
width = int(rect[1][0])
height = int(rect[1][1])
src_pts = box.astype("float32")
dst_pts = np.array([[0, height-1], [0, 0],
[width-1, 0], [width-1, height-1]], dtype="float32")
M = cv2.getPerspectiveTransform(src_pts, dst_pts)
warped = cv2.warpPerspective(image, M, (width, height))
plt.imshow(warped)
If you have a look at some of the images in the folder, those are the inputs. When I run these images through the code above, I get an output like this. Some of them are cropped nicely (straightened), however, some of them are cropped with 90 degree rotations. Is there a code to counter the ‘rotating 90 degrees output’ problem?
Here are some images:
Image Inputs: Four X-ray examples
Image Outputs: Returns images that are 90 degrees rotated
Image Outputs wanted: Straightened image (Just used Photoshop to straighten them. Dont want to do this for 10000 images…)
UPDATE:
I edited the code according to below-mentioned suggestions. After running the some samples, it now returns images that are now 90 degrees slanted to the right.
I doubt it’s because of the quality of the images. Maybe it’s got to do with OpenCV’s minAreaRect()
? or boxPoints
?
FINAL UPDATE:
According to @Prashant Maurya, the code was updated with a function added to detect whether the position of the hand is left or right. And then mapping src_pts to right dst_pts. Full code is shown below.
Hi there are two changes which will correct the output:
- The width and height taken in the code is in the wrong order ie: width: 1470 & height: 1118 just switch the values:
- Map src_pts to right dst_pts the current code is mapping top left
corner to bottom left therefore the image is being rotated. - Added function to detect whether image is right tilted or left and rotate and rotate it accordingly
Full code with changes is:
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image, create mask, grayscale, Gaussian blur, Otsu's threshold
img_path = "xray1.png"
image = cv2.imread(img_path)
cv2.imshow("image original", image)
cv2.waitKey(10000)
original = image.copy()
blank = np.zeros(image.shape[:2], dtype = np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (33,33), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Merge text into a single contour
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations = 3)
# Find contours
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key = lambda x: cv2.boundingRect(x)[0])
def get_tilt(box):
tilt = "Left"
x_list = [coord[0] for coord in box]
y_list = [coord[1] for coord in box]
print(x_list)
print(y_list)
x_list = sorted(x_list)
y_list = sorted(y_list)
print(x_list)
print(y_list)
for coord in box:
if coord[0] == x_list[0]:
index = y_list.index(coord[1])
print("Index: ", index)
if index == 1:
tilt = "Left"
else:
tilt = "Right"
return tilt
for c in cnts:
# Filter using contour area and aspect ratio (x1 = width, y1 = height)
x, y, x1, y1 = cv2.boundingRect(c)
if (x1 > 500) and (y1 > 700):
rect = cv2.minAreaRect(c)
print("rect",rect)
box = cv2.boxPoints(rect)
box = np.int0(box)
# print("rect:", box)
tilt = get_tilt(box)
src_pts = box.astype("float32")
if tilt == "Left":
width = int(rect[1][1])
height = int(rect[1][0])
dst_pts = np.array([[0, 0],
[width-1, 0], [width-1, height-1], [0, height-1]], dtype="float32")
else:
width = int(rect[1][0])
height = int(rect[1][1])
dst_pts = np.array([[0, height-1], [0, 0],
[width-1, 0], [width-1, height-1]], dtype="float32")
print("Src pts:", src_pts)
print("Dst pts:", dst_pts)
M = cv2.getPerspectiveTransform(src_pts, dst_pts)
warped = cv2.warpPerspective(image, M, (width, height))
print("Showing image ..")
# plt.imshow(warped)
cv2.imshow("image crop", warped)
cv2.waitKey(10000)