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tester.py
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tester.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Mar 10 10:52:22 2024
@author: Pratham Gupta
"""
#This is for testing of filters and basic image processing
import cv2
import numpy as np
loadcopy = load = cv2.imread('testImages/test4.jpg')
down_width = 720
down_height = 720
down_points = (down_width, down_height)
image = cv2.resize(load, down_points, interpolation= cv2.INTER_LINEAR)
imagecopy = cv2.resize(loadcopy, down_points, interpolation= cv2.INTER_LINEAR)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(gray,400,500)
# Threshold for an optimal value, it may vary depending on the image.
image[dst>0.012*dst.max()]=[255,255,255]
offset = cv2.cvtColor(image-imagecopy, cv2.COLOR_BGR2GRAY)
# cv2.imshow('Offset',offset)
# cv2.waitKey()
column_sum=[]
for i in range(down_height):
column_sum.append(np.sum(offset[:,i]))
#print(column_sum)
#print(max(column_sum))
lvline_index =column_sum.index(max(column_sum))
loopline2_index=lvline_index
while abs(loopline2_index-lvline_index)<10:
column_sum.pop(loopline2_index)
loopline2_index = column_sum.index(max(column_sum))
columntestline_index = loopline2_index
if columntestline_index < lvline_index:
vline_index = columntestline_index
else:
vline_index=columntestline_index+1
row_sum=[]
for i in range(down_width):
row_sum.append(np.sum(offset[i,:]))
#print(row_sum)
#print(max(row_sum))
bhline_index =row_sum.index(max(row_sum))
loopline_index=bhline_index
while abs(loopline_index-bhline_index)<10:
row_sum.pop(loopline_index)
loopline_index = row_sum.index(max(row_sum))
rowtestline_index = loopline_index
if rowtestline_index < bhline_index:
hline_index = rowtestline_index
else:
hline_index=rowtestline_index+1
a = lvline_index
b = hline_index
c = vline_index
d = bhline_index
if b>d:
switch = b
b = d
d = switch
if c>a:
switch = c
c = a
a = switch
offset[b,c:a] =128
offset[b:d,a] =128
offset[b:d,c] =128
offset[d,c:a] =128
# cv2.imshow('Offset', offset)
# cv2.waitKey()
imagecopy[b,c:a] =(0,255,0)
imagecopy[d,c:a] =(0,255,0)
imagecopy[b:d,a] =(0,255,0)
imagecopy[b:d,c] =(0,255,0)
cv2.imshow('Image',imagecopy)
cv2.waitKey()
graph = imagecopy[(b-3):(d+3),(c-3):(a+3)]
cv2.imshow("Cropped", graph)
cv2.waitKey()
nongraph = imagecopy
nongraph[b-3:d-3,c+5:a+5] = 255
graphcopy = graph
# Display cropped image
cv2.imshow("Cropped", graph)
cv2.waitKey()
# Save the cropped image
#cv2.imwrite("testImages/Cropped Image.jpg", cropped_image)
graygraph= cv2.cvtColor(graph, cv2.COLOR_BGR2GRAY)
graygraphcopy = cv2.cvtColor(graphcopy,cv2.COLOR_BGR2GRAY)
graygraph = np.float32(graygraph)
dst = cv2.cornerHarris(graygraph,2,3,0.05)
# Look into Shi-Tomasi corner detection
# Threshold for an optimal value, it may vary depending on the image.
graygraph[dst>0.06*dst.max()]=255
corneronly = graygraph-graygraphcopy
corneronly[:-9,9:]=0
#cv2.imshow('Corneronly', corneronly)
#cv2.waitKey()
(var1,var2) = np.shape(corneronly)
indexhighlow= []
indexlowhigh= []
for i in range(var2-1):
for j in range(var1-1):
if corneronly[j,i] > 128:
if corneronly[j+1,i] < 128:
indexhighlow.append((j,i))
elif corneronly[j,i] < 128:
if corneronly[j+1,i] > 128:
indexlowhigh.append((j+1,i))
second_element_count = {}
for tup in indexlowhigh:
second_element = tup[1]
if second_element in second_element_count:
second_element_count[second_element] += 1
else:
second_element_count[second_element] = 1
max_occurrence = max(second_element_count.values())
max_occurrence_elements = [k for k, v in second_element_count.items() if v == max_occurrence]
maximum = max_occurrence_elements[0]
filtered_array = [tup for tup in indexlowhigh if tup[1] == maximum]
print(filtered_array)
differences = []
for i in range(1, len(filtered_array)):
difference = filtered_array[i][0] - filtered_array[i - 1][0]
differences.append(difference)
print(differences)
def positive(arr):
return[x for x in arr if x>5]
differences=positive(differences)
lengthscaley = np.ceil(np.mean(differences))
print(lengthscaley)
for i in range(var2-1):
for j in range(var1-1):
if corneronly[j,i] > 128:
if corneronly[j,i+1] < 128:
indexhighlow.append((j,i))
elif corneronly[j,i] < 128:
if corneronly[j,i+1] > 128:
indexlowhigh.append((j,i+1))
first_element_count = {}
for tup in indexlowhigh:
first_element = tup[0]
if first_element in first_element_count:
first_element_count[first_element] += 1
else:
first_element_count[first_element] = 1
max_occurrence = max(first_element_count.values())
max_occurrence_elements = [k for k, v in first_element_count.items() if v == max_occurrence]
maximum = max_occurrence_elements[0]
filtered_array = [tup for tup in indexlowhigh if tup[0] == maximum]
print(filtered_array)
differences = []
for i in range(1, len(filtered_array)):
difference = filtered_array[i][1] - filtered_array[i - 1][1]
differences.append(difference)
print(differences)
differences=positive(differences)
lengthscalex = np.ceil(np.mean(differences))
print(lengthscalex)