I tried to sort out the objects from the image of the steak set meal --③ Similar image Heat map detection
Introduction
Continuing from I tried to sort out objects from the images of the steak set meal-several overlaps, this time I compared the histograms to detect similar images. I went.
reference
-Calculate image similarity with Python + OpenCV
Source code
- There are some parts that have not been refactored, but I hope you can ignore them.
By the way, this time we will put together rectangles of almost the same image size, so we have not dared to resize the image.
group_image.py
# -*- coding: utf-8 -*-
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import selectivesearch
import os
def main():
# loading lena image
img = cv2.imread("{Image file}")
# perform selective search
img_lbl, regions = selectivesearch.selective_search(
img,
scale=500,
sigma=0.9,
min_size=10
)
candidates = set()
for r in regions:
# excluding same rectangle (with different segments)
if r['rect'] in candidates:
continue
# excluding regions smaller than 2000 pixels
if r['size'] < 2000:
continue
# distorted rects
x, y, w, h = r['rect']
if w / h > 1.2 or h / w > 1.2:
continue
candidates.add(r['rect'])
# draw rectangles on the original image
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(img)
overlaps = {}
#Count the number of overlaps and assign them to the array.
for x, y, w, h in candidates:
group = x + y + w + h
for x2, y2, w2, h2 in candidates:
if x2 - w < x < x2 + w2 and y2 - h < y < y2 + h2:
if not group in overlaps:
overlaps[group] = 0
overlaps[group] = overlaps[group] + 1
#Outputs files with 30 or more overlaps (30 is arbitrarily thresholded).
for key, overlap in enumerate(overlaps):
if overlap > 30:
for x, y, w, h in candidates:
group = x + y + w + h
if group in overlaps:
cv2.imwrite("{File Path}" + str(group) + '.jpg', img[y:y + h, x:x + w])
#Calculate the similarity of images by comparing histograms
image_dir = "{File Path}/"
target_files = os.listdir(image_dir)
files = os.listdir(image_dir)
for target_file in target_files:
if target_file == '.DS_Store':
continue
target_image_path = image_dir + target_file
target_image = cv2.imread(target_image_path)
target_hist = cv2.calcHist([target_image], [0], None, [256], [0, 256])
for file in files:
if file == '.DS_Store' or file == target_file:
continue
comparing_image_path = image_dir + file
comparing_image = cv2.imread(comparing_image_path)
comparing_hist = cv2.calcHist([comparing_image], [0], None, [256], [0, 256])
ret = cv2.compareHist(target_hist, comparing_hist, 0)
probability = ret * 100
print("target file: " + target_file, "file: " + file, "similarity: " + str(probability) + "%")
if __name__ == "__main__":
main()
result
The one with 100% similarity seems to be the exact same image.
- Sometimes it's negative, but ...
('target file: 1018.jpg', 'file: 250.jpg', 'similarity: 24.8911807562%')
('target file: 1018.jpg', 'file: 369.jpg', 'similarity: 6.78223462382%')
('target file: 1018.jpg', 'file: 389.jpg', 'similarity: 11.1974626968%')
('target file: 1018.jpg', 'file: 432.jpg', 'similarity: 35.179639392%')
('target file: 1018.jpg', 'file: 463.jpg', 'similarity: 79.5281353144%')
('target file: 1018.jpg', 'file: 477.jpg', 'similarity: 51.5870749875%')
('target file: 1018.jpg', 'file: 480.jpg', 'similarity: 55.1832671208%')
('target file: 1018.jpg', 'file: 492.jpg', 'similarity: 88.2822944972%')
('target file: 1018.jpg', 'file: 522.jpg', 'similarity: 76.9528435542%')
('target file: 1018.jpg', 'file: 547.jpg', 'similarity: 84.9997652385%')
('target file: 1018.jpg', 'file: 559.jpg', 'similarity: 77.6441098189%')
('target file: 1018.jpg', 'file: 575.jpg', 'similarity: 76.3571281251%')
('target file: 1018.jpg', 'file: 581.jpg', 'similarity: 76.7456283874%')
('target file: 1018.jpg', 'file: 594.jpg', 'similarity: 31.9957806646%')
('target file: 1018.jpg', 'file: 603.jpg', 'similarity: 85.3813480299%')
('target file: 1018.jpg', 'file: 629.jpg', 'similarity: 88.0957855275%')
('target file: 1018.jpg', 'file: 632.jpg', 'similarity: 60.7236277665%')
('target file: 1018.jpg', 'file: 634.jpg', 'similarity: 62.3073009307%')
('target file: 1018.jpg', 'file: 635.jpg', 'similarity: 65.5935422037%')
('target file: 1018.jpg', 'file: 657.jpg', 'similarity: 56.6421422253%')
('target file: 1018.jpg', 'file: 658.jpg', 'similarity: 82.0967550779%')
('target file: 1018.jpg', 'file: 659.jpg', 'similarity: 89.7396556858%')
('target file: 1018.jpg', 'file: 754.jpg', 'similarity: 78.3236083079%')
('target file: 1018.jpg', 'file: 758.jpg', 'similarity: 79.0903410039%')
('target file: 1018.jpg', 'file: 799.jpg', 'similarity: 89.3025985059%')
('target file: 1018.jpg', 'file: 806.jpg', 'similarity: 97.2873823376%')
('target file: 1018.jpg', 'file: 815.jpg', 'similarity: 93.3345515745%')
('target file: 1018.jpg', 'file: 867.jpg', 'similarity: 81.8261095798%')
('target file: 1018.jpg', 'file: 920.jpg', 'similarity: 93.4987208053%')
('target file: 1018.jpg', 'file: 921.jpg', 'similarity: 90.3518029292%')
('target file: 1018.jpg', 'file: 932.jpg', 'similarity: 94.4258967857%')
('target file: 1018.jpg', 'file: 964.jpg', 'similarity: 10.5652113467%')
('target file: 1018.jpg', 'file: 972.jpg', 'similarity: 98.8755231495%')
----The following is omitted
Details
What is the similarity of 90% or more?
('target file: 1018.jpg', 'file: 972.jpg', 'similarity: 98.8755231495%')
('target file: 754.jpg', 'file: 758.jpg', 'similarity: 99.8932682258%')
I feel that the accuracy is quite good.
Summary
It seems that I got a pretty good result once, so next time I would like to group images with high similarity and put them together.
All page links
-I tried object detection using Python and OpenCV -I tried to sort out objects from the image of steak set meal-① Object detection -I tried to sort out the objects from the image of the steak set meal-② Overlap number sorting -I tried to sort out the objects from the image of the steak set meal-③ Similar image heat map detection -I tried to sort out the objects from the image of the steak set meal-④ Clustering -I tried to sort out objects from the image of steak set meal-⑤ Similar image feature point detection edition
Recommended Posts