mode=cv.RETR_TREE,
method=cv.CHAIN_APPROX_NONE)
largest_contour_index = my_functions.get_contour_max_area(contours)[0]
# only print if interested
# print(largest_contour_index)
if largest_contour_index == 0:
continue
largest_contoured_polygon = cv.approxPolyDP(
contours[largest_contour_index],
0.05 * cv.arcLength(contours[largest_contour_index], True), True)
frame_BGR_cropped = my_functions.crop_image(frame_BGR_resized,
largest_contoured_polygon)
# Displaying images except the original before resizing
my_functions.open_in_location(frame_BGR_resized, "Original Frame Scaled",
0, 10)
my_functions.open_in_location(frame_GRAY_blured, "Gray Filtered Frame",
400, 10)
my_functions.open_in_location(frame_THRESHOLDED, "Thresholded Frame", 800,
10)
my_functions.open_in_location(frame_BGR_cropped, "Original Frame Cropped",
1200, 10)
cv.waitKey()
cv.destroyAllWindows()
frame_canny = cv.Canny(frame_GRAY, lower_TH_level, upper_TH_level)
lines = cv.HoughLinesP(frame_canny,
1,
np.pi / 90,
80,
minLineLength=100,
maxLineGap=25)
if lines is None:
print("error, image has no lines")
continue
for line in lines:
x1, y1, x2, y2 = line[0]
cv.line(frame_BGR_resized_2, (x1, y1), (x2, y2), (0, 255, 0), 2)
Hough_points = my_functions.hough_square_absolute_maximum(lines)
frame_BGR_Cropped = frame_BGR_resized[Hough_points[1]:Hough_points[3],
Hough_points[0]:Hough_points[2]]
# Displaying images except the original before resizing
my_functions.open_in_location(frame_BGR_resized, "Original Frame Scaled",
0, 10)
my_functions.open_in_location(frame_canny, "Canny Edge", 400, 10)
my_functions.open_in_location(frame_BGR_resized_2, "Hough Lines", 800, 10)
my_functions.open_in_location(frame_BGR_Cropped, "Original Frame Cropped",
1200, 10)
cv.waitKey()
cv.destroyAllWindows()
#My fucntions
import my_functions
directory=r'D:\learning\Semesters\Semester 8\Image_Processing\Project\Chess_board_sampels'
imges= my_functions.load_images_from_source(directory,2)
for i in range(len(imges)):
print("Chess_image_"+str(i+1))
org =my_functions.resize_image(imges[i],10)
org_2 = org
img=cv.cvtColor(org,cv.COLOR_BGR2GRAY)
my_functions.open_in_location(img,"Chess_image_"+str(i+1),-1347,-165)
canny= cv.Canny(img, 80, 150)
#canny_2=canny
blur = cv.GaussianBlur(img, (5, 5), 3)
my_functions.open_in_location(blur,'Gaussian Blur '+str(i+1),-905,-165)
thrsh = cv.adaptiveThreshold(blur, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY , 11,2)
#_, img = cv.threshold(img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
contours, hierarchy = cv.findContours(thrsh, mode = cv.RETR_TREE, method = cv.CHAIN_APPROX_NONE)
max = (my_functions.get_contour_max_area(contours))[0]
if max==0:
continue
import numpy as np
# My fucntions
import my_functions
number_of_images_in_source = 10
source = r'D:\learning\Semesters\Semester 8\Image_Processing\Project\Chess_board_sampels_3'
directory = r'D:\learning\Semesters\Semester 8\Image_Processing\Project\all_cropped'
frame_BGR_original = my_functions.load_images_from_source(source, number_of_images_in_source - 1)
for c in range(number_of_images_in_source):
frame_BGR_resized = my_functions.resize_image(frame_BGR_original[c], 10)
frame_BGR_resized_2=frame_BGR_resized
my_functions.open_in_location(frame_BGR_resized, "Original Frame Scaled", 0, 10)
frame_GRAY = cv.cvtColor(frame_BGR_resized, cv.COLOR_BGR2GRAY)
frame_GRAY_blured=cv.GaussianBlur(frame_GRAY,(5,5),0)
GRAY_corners = cv.goodFeaturesToTrack(frame_GRAY, 100, 0.4, 5)
corners_array = np.int0(GRAY_corners)
#Display the corners found int he image
for i in corners_array:
x, y = i.ravel()
cv.circle(frame_BGR_resized_2, (x, y), 3, [255, 255, 0], -1)
import pause
#My fucntions
import my_functions
directory = r'D:\learning\Semesters\Semester 8\Image_Processing\Project\Chess_board_sampels_2'
imges = my_functions.load_images_from_source(directory, 50)
for i in range(len(imges)):
print("Chess_image_" + str(i + 1))
org = my_functions.resize_image(imges[i], 10)
org_2 = org
img = cv.cvtColor(org, cv.COLOR_BGR2GRAY)
my_functions.open_in_location(img, "Chess_image_" + str(i + 1), -1347,
-165)
canny = cv.Canny(img, 80, 150)
#canny_2=canny
blur = cv.GaussianBlur(img, (5, 5), 3)
thrsh = cv.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
'''
lines = cv.HoughLinesP(thrsh, 1, np.pi / 90, 220, minLineLength=200, maxLineGap=8)
if lines is None:
print("error, image has no lines")
continue
for line in lines:
x1, y1, x2, y2 = line[0]
cv.line(org_2, (x1, y1), (x2, y2), (0, 255, 0), 2)