зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-30 13:36:04 +02:00
b585097f19
* reduce code smells * account for transl. version change * remove print statements * remove imageai
277 строки
8.4 KiB
Python
277 строки
8.4 KiB
Python
import os
|
||
import ntpath
|
||
from PIL import Image
|
||
from matplotlib.patches import ConnectionPatch
|
||
import cv2
|
||
import pandas as pd
|
||
import numpy as np
|
||
import matplotlib.pyplot as plt
|
||
|
||
|
||
# use this function to visualize the matches
|
||
def plot_matches(img1, img2, keypoints1, keypoints2):
|
||
fig, axes = plt.subplots(1, 2, figsize=(16, 7))
|
||
|
||
# draw images
|
||
axes[0].imshow(img1)
|
||
axes[1].imshow(img2)
|
||
|
||
# draw matches
|
||
for kp1, kp2 in zip(keypoints1, keypoints2):
|
||
c = np.random.rand(3)
|
||
con = ConnectionPatch(
|
||
xyA=kp1,
|
||
coordsA=axes[0].transData,
|
||
xyB=kp2,
|
||
coordsB=axes[1].transData,
|
||
color=c,
|
||
)
|
||
fig.add_artist(con)
|
||
axes[0].plot(*kp1, color=c, marker="x")
|
||
axes[1].plot(*kp2, color=c, marker="x")
|
||
|
||
plt.show()
|
||
|
||
|
||
# use this function to visualize the matches from sift
|
||
def draw_matches(matches, img1, img2, kp1, kp2):
|
||
MIN_MATCH_COUNT = 4
|
||
if len(matches) > MIN_MATCH_COUNT:
|
||
# Estimate homography between template and scene
|
||
src_pts = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
|
||
dst_pts = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
|
||
|
||
M = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)[0]
|
||
|
||
# Draw detected template in scene image
|
||
h = img1.shape[0]
|
||
w = img1.shape[1]
|
||
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(
|
||
-1, 1, 2
|
||
)
|
||
dst = cv2.perspectiveTransform(pts, M)
|
||
|
||
img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
|
||
|
||
h1 = img1.shape[0]
|
||
h2 = img2.shape[0]
|
||
w1 = img1.shape[1]
|
||
w2 = img2.shape[1]
|
||
nwidth = w1 + w2
|
||
nheight = max(h1, h2)
|
||
hdif = int((h2 - h1) / 2)
|
||
newimg = np.zeros((nheight, nwidth, 3), np.uint8)
|
||
|
||
for i in range(3):
|
||
newimg[hdif : hdif + h1, :w1, i] = img1
|
||
newimg[:h2, w1 : w1 + w2, i] = img2
|
||
|
||
# Draw SIFT keypoint matches
|
||
for m in matches:
|
||
pt1 = (int(kp1[m.queryIdx].pt[0]), int(kp1[m.queryIdx].pt[1] + hdif))
|
||
pt2 = (int(kp2[m.trainIdx].pt[0] + w1), int(kp2[m.trainIdx].pt[1]))
|
||
cv2.line(newimg, pt1, pt2, (255, 0, 0))
|
||
|
||
plt.imshow(newimg)
|
||
plt.show()
|
||
else:
|
||
print("Not enough matches are found - %d/%d" % (len(matches), MIN_MATCH_COUNT))
|
||
|
||
|
||
# compute matches from sift
|
||
def matching_points(img1, img2):
|
||
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
|
||
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
|
||
sift = cv2.xfeatures2d.SIFT_create()
|
||
kp1, des1 = sift.detectAndCompute(img1, None)
|
||
kp2, des2 = sift.detectAndCompute(img2, None)
|
||
des1 = np.float32(des1)
|
||
des2 = np.float32(des2)
|
||
# Initialize and use FLANN
|
||
FLANN_INDEX_KDTREE = 1
|
||
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
|
||
search_params = dict(checks=50)
|
||
flann = cv2.FlannBasedMatcher(index_params, search_params)
|
||
matches = flann.knnMatch(des1, des2, k=2)
|
||
|
||
filtered_matches = []
|
||
for m, n in matches:
|
||
if m.distance < 0.7 * n.distance:
|
||
filtered_matches.append(m)
|
||
|
||
# draw_matches(filtered_matches, img1, img2, kp1, kp2)
|
||
|
||
return filtered_matches, kp1, kp2
|
||
|
||
|
||
# extract match points from matches
|
||
def kp_from_matches(matches, kp1, kp2):
|
||
kp1 = np.float32([kp1[m.queryIdx].pt for m in matches])
|
||
kp2 = np.float32([kp2[m.trainIdx].pt for m in matches])
|
||
return kp1, kp2
|
||
|
||
|
||
# estimate a crop corner for posts image via matches
|
||
def compute_crop_corner(
|
||
matches, kp1, kp2, region=30, h_margin=28, v_margin=5, min_match=6
|
||
):
|
||
kp1, kp2 = kp_from_matches(matches, kp1, kp2)
|
||
ys = kp2[:, 1]
|
||
covers = []
|
||
for y in ys:
|
||
ys_c = ys - y
|
||
series = pd.Series(ys_c)
|
||
is_between = series.between(0, region)
|
||
covers.append(is_between.sum())
|
||
|
||
covers = np.array(covers)
|
||
if covers.max() < min_match:
|
||
return None
|
||
|
||
kp_id = ys[covers.argmax()]
|
||
v = int(kp_id) - v_margin if int(kp_id) > v_margin else int(kp_id)
|
||
hs = []
|
||
for kp in kp2:
|
||
if 0 <= kp[1] - v <= region:
|
||
hs.append(kp[0])
|
||
|
||
h = int(np.min(hs)) - h_margin if int(np.min(hs)) > h_margin else 0
|
||
|
||
return v, h
|
||
|
||
|
||
# crop the posts image
|
||
def crop_posts_image(
|
||
ref_view, view, plt_match=False, plt_crop=False, correct_margin=700
|
||
):
|
||
"""
|
||
get file lists from dir and sub dirs
|
||
ref_view：ref_view for crop the posts images
|
||
view: posts image that need cropping
|
||
rte： None - not cropped, or (crop_view, number of matches)
|
||
"""
|
||
filtered_matches, kp1, kp2 = matching_points(ref_view, view)
|
||
MIN_MATCH_COUNT = 6
|
||
if len(filtered_matches) < MIN_MATCH_COUNT:
|
||
return None
|
||
|
||
if plt_match:
|
||
img1 = cv2.cvtColor(ref_view, cv2.COLOR_BGR2GRAY)
|
||
img2 = cv2.cvtColor(view, cv2.COLOR_BGR2GRAY)
|
||
draw_matches(filtered_matches, img1, img2, kp1, kp2)
|
||
|
||
corner = compute_crop_corner(filtered_matches, kp1, kp2)
|
||
if corner is None:
|
||
return None
|
||
v, h = corner
|
||
if view.shape[1] - h > correct_margin:
|
||
h = view.shape[1] - ref_view.shape[1]
|
||
if view.shape[1] - h < ref_view.shape[1]:
|
||
h = view.shape[1] - ref_view.shape[1]
|
||
|
||
crop_view = view[0:v, h:, :]
|
||
if plt_crop:
|
||
view[v, :, 0:3] = [255, 0, 0]
|
||
view[:, h, 0:3] = [255, 0, 0]
|
||
plt.imshow(view)
|
||
plt.show()
|
||
|
||
plt.imshow(crop_view)
|
||
plt.show()
|
||
|
||
return crop_view, len(filtered_matches)
|
||
|
||
|
||
def get_file_list(dir, filelist, ext=None, convert_unix=True):
|
||
"""
|
||
get file lists from dir and sub dirs
|
||
dir：root dir for file lists
|
||
ext: file extension
|
||
rte： File list
|
||
"""
|
||
if os.path.isfile(dir):
|
||
if ext is None:
|
||
filelist.append(dir)
|
||
else:
|
||
if ext in dir[-3:]:
|
||
filelist.append(dir)
|
||
|
||
elif os.path.isdir(dir):
|
||
for s in os.listdir(dir):
|
||
new_dir = os.path.join(dir, s)
|
||
get_file_list(new_dir, filelist, ext)
|
||
|
||
if convert_unix:
|
||
new_filelist = []
|
||
for file_ in filelist:
|
||
file_ = file_.replace("\\", "/")
|
||
new_filelist.append(file_)
|
||
return new_filelist
|
||
else:
|
||
return filelist
|
||
|
||
|
||
def crop_posts_from_refs(ref_views, view, plt_match=False, plt_crop=False):
|
||
crop_view = None
|
||
max_matchs = 0
|
||
for ref_view in ref_views:
|
||
rte = crop_posts_image(ref_view, view, plt_match=plt_match, plt_crop=plt_crop)
|
||
if rte is not None:
|
||
crop_img, match_num = rte
|
||
if match_num > max_matchs:
|
||
crop_view = crop_img
|
||
max_matchs = match_num
|
||
# print("match_num = ", match_num)
|
||
|
||
return crop_view
|
||
|
||
|
||
def crop_posts_from_files(
|
||
ref_dir, crop_dir, save_crop_dir, plt_match=False, plt_crop=False
|
||
):
|
||
ref_list = []
|
||
ref_list = get_file_list(ref_dir, ref_list, ext="png")
|
||
ref_views = []
|
||
for ref_file in ref_list:
|
||
ref_view = np.array(Image.open(ref_file))
|
||
ref_views.append(ref_view)
|
||
crop_list = []
|
||
crop_list = get_file_list(crop_dir, crop_list, ext="png")
|
||
|
||
for crop_file in crop_list:
|
||
view = np.array(Image.open(crop_file))
|
||
crop_view = crop_posts_from_refs(
|
||
ref_views, view, plt_match=plt_match, plt_crop=plt_crop
|
||
)
|
||
if crop_view is not None:
|
||
filename = ntpath.basename(crop_file)
|
||
save_path = os.path.join(save_crop_dir, filename)
|
||
save_path = save_path.replace("\\", "/")
|
||
cv2.imwrite(save_path, crop_view)
|
||
|
||
|
||
def test_crop_from_file():
|
||
# Load images
|
||
# view1 = np.array(Image.open("data/ref/ref-00.png")) / 255
|
||
# view2 = np.array(Image.open("data/napsa/100539_ben.png")) / 255
|
||
view1 = np.array(Image.open("data/ref/ref-06.png"))
|
||
view2 = np.array(Image.open("data/napsa/102956_eng.png"))
|
||
crop_view, match_num = crop_posts_image(view1, view2, plt_match=True, plt_crop=True)
|
||
cv2.imwrite("data/crop_100489_ind.png", crop_view)
|
||
|
||
|
||
def test_crop_from_folder():
|
||
ref_dir = "./data/ref"
|
||
crop_dir = "./data/apsa"
|
||
save_crop_dir = "data/crop"
|
||
crop_posts_from_files(
|
||
ref_dir, crop_dir, save_crop_dir, plt_match=False, plt_crop=False
|
||
)
|
||
|
||
|
||
# do tests:
|
||
|
||
# test_crop_from_file()
|
||
|
||
# test_crop_from_folder()
|