Python+OpenCV实现图像识别替换功能详解

OpenCV-Python是一个Python库,旨在解决计算机视觉问题。

OpenCV是一个开源的计算机视觉库,1999年由英特尔的Gary Bradski启动。Bradski在访学过程中注意到,在很多优秀大学的实验室中,都有非常完备的内部公开的计算机视觉接口。这些接口从一届学生传到另一届学生,对于刚入门的新人来说,使用这些接口比重复造轮子方便多了。这些接口可以让他们在之前的基础上更有效地开展工作。OpenCV正是基于为计算机视觉提供通用接口这一目标而被策划的。

安装opencv

pip3 install -i https://pypi.doubanio.com/simple/ opencv-python

思路:

1、首先区分三张图片:

base图片代表初始化图片;

template图片代表需要在大图中匹配的图片;

white图片为需要替换的图片。

Python+OpenCV实现图像识别替换功能详解

2、然后template图片逐像素缩小匹配,设定阈值,匹配度到达阈值的图片,判定为在初始图片中;否则忽略掉。

3、匹配到最大阈值的地方,返回该区域的位置(x,y)

4、然后用white图片resize到相应的大小,填补到目标区域。

match函数:

"""检查模板图片中是否包含目标图片"""
def make_cv2(photo1, photo2):
global x, y, w, h, num_1,flag
starttime = datetime.datetime.now()
#读取base图片
img_rgb = cv2.imread(f'{photo1}')
#读取template图片
template = cv2.imread(f'{photo2}')
h, w = template.shape[:-1]
print('初始宽高', h, w)
res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)
print('初始最大相似度', res.max())
threshold = res.max()
""",相似度小于0.2的,不予考虑;相似度在[0.2-0.75]之间的,逐渐缩小图片"""
print(threshold)
while threshold >= 0.1 and threshold <= 0.83:
if w >= 20 and h >= 20:
w = w - 1
h = h - 1
template = cv2.resize(
template, (w, h), interpolation=cv2.INTER_CUBIC)
res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)
threshold = res.max()
print('宽度:', w, '高度:', h, '相似度:', threshold)
else:
break
"""达到0.75覆盖之前的图片"""
if threshold > 0.8:
loc = np.where(res >= threshold)
x = int(loc[1])
y = int(loc[0])
print('覆盖图片左上角坐标:', x, y)
for pt in zip(*loc[::-1]):
cv2.rectangle(
img_rgb, pt, (pt[0] + w, pt[1] + h), (255, 144, 51), 1)
num_1 += 1
endtime = datetime.datetime.now()
print("耗时:", endtime - starttime)
overlay_transparent(x, y, photo1, photo3)
else:
flag = False

replace函数:

"""将目标图片镶嵌到指定坐标位置"""
def overlay_transparent(x, y, photo1, photo3):
#覆盖图片的时候上下移动的像素空间
y += 4
global w, h, num_2
background = cv2.imread(f'{photo1}')
overlay = cv2.imread(f'{photo3}')
"""缩放图片大小"""
overlay = cv2.resize(overlay, (w, h), interpolation=cv2.INTER_CUBIC)
background_width = background.shape[1]
background_height = background.shape[0]
if x >= background_width or y >= background_height:
return background
h, w = overlay.shape[0], overlay.shape[1]
if x + w > background_width:
w = background_width - x
overlay = overlay[:, :w]
if y + h > background_height:
h = background_height - y
overlay = overlay[:h]
if overlay.shape[2] < 4:
overlay = np.concatenate([overlay, np.ones((overlay.shape[0], overlay.shape[1], 1), dtype=overlay.dtype) * 255],axis=2,)
overlay_image = overlay[..., :3]
mask = overlay[..., 3:] / 255.0
background[y:y + h,x:x + w] = (1.0 - mask) * background[y:y + h,x:x + w] + mask * overlay_image
# path = 'result'
path = ''
cv2.imwrite(os.path.join(path, f'1.png'), background)
num_2 += 1
print('插入成功。')
init()

每次执行需要初始化x,y(图片匹配初始位置参数),w,h(图片缩放初始宽高)

x = 0
y = 0
w = 0
h = 0
flag = True
threshold = 0
template = ''
num_1 = 0
num_2 = 0
photo3 = ''
"""参数初始化"""
def init():
global x, y, w, h, threshold, template,flag
x = 0
y = 0
w = 0
h = 0
threshold = 0
template = ''

完整代码

import cv2
import datetime
import os
import numpy as np
x = 0
y = 0
w = 0
h = 0
flag = True
threshold = 0
template = ''
num_1 = 0
num_2 = 0
photo3 = ''
"""参数初始化"""
def init():
global x, y, w, h, threshold, template,flag
x = 0
y = 0
w = 0
h = 0
threshold = 0
template = ''
"""检查模板图片中是否包含目标图片"""
def make_cv2(photo1, photo2):
global x, y, w, h, num_1,flag
starttime = datetime.datetime.now()
img_rgb = cv2.imread(f'{photo1}')
template = cv2.imread(f'{photo2}')
h, w = template.shape[:-1]
print('初始宽高', h, w)
res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)
print('初始最大相似度', res.max())
threshold = res.max()
""",相似度小于0.2的,不予考虑;相似度在[0.2-0.75]之间的,逐渐缩小图片"""
print(threshold)
while threshold >= 0.1 and threshold <= 0.83:
if w >= 20 and h >= 20:
w = w - 1
h = h - 1
template = cv2.resize(
template, (w, h), interpolation=cv2.INTER_CUBIC)
res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)
threshold = res.max()
print('宽度:', w, '高度:', h, '相似度:', threshold)
else:
break
"""达到0.75覆盖之前的图片"""
if threshold > 0.8:
loc = np.where(res >= threshold)
x = int(loc[1])
y = int(loc[0])
print('覆盖图片左上角坐标:', x, y)
for pt in zip(*loc[::-1]):
cv2.rectangle(
img_rgb, pt, (pt[0] + w, pt[1] + h), (255, 144, 51), 1)
num_1 += 1
endtime = datetime.datetime.now()
print("耗时:", endtime - starttime)
overlay_transparent(x, y, photo1, photo3)
else:
flag = False
"""将目标图片镶嵌到指定坐标位置"""
def overlay_transparent(x, y, photo1, photo3):
y += 0
global w, h, num_2
background = cv2.imread(f'{photo1}')
overlay = cv2.imread(f'{photo3}')
"""缩放图片大小"""
overlay = cv2.resize(overlay, (w, h), interpolation=cv2.INTER_CUBIC)
background_width = background.shape[1]
background_height = background.shape[0]
if x >= background_width or y >= background_height:
return background
h, w = overlay.shape[0], overlay.shape[1]
if x + w > background_width:
w = background_width - x
overlay = overlay[:, :w]
if y + h > background_height:
h = background_height - y
overlay = overlay[:h]
if overlay.shape[2] < 4:
overlay = np.concatenate([overlay, np.ones((overlay.shape[0], overlay.shape[1], 1), dtype=overlay.dtype) * 255],axis=2,)
overlay_image = overlay[..., :3]
mask = overlay[..., 3:] / 255.0
background[y:y + h,x:x + w] = (1.0 - mask) * background[y:y + h,x:x + w] + mask * overlay_image
# path = 'result'
path = ''
cv2.imwrite(os.path.join(path, f'1.png'), background)
num_2 += 1
print('插入成功。')
init()
if __name__ == "__main__":
photo1 = "1.png"
photo2 = "3.png"
photo3 = "white.png"
while flag == True:
make_cv2(photo1, photo2)
overlay_transparent(x, y, photo1, photo3)

执行结果:

Python+OpenCV实现图像识别替换功能详解