Ai Python 视觉识别opencv Computer Vision Library 3模式匹配match Template 增加缩放功能

refer to:
https://pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/

结论：

最好还是用手动截取的template

缩放截取的，匹配度比较低。

关键代码:

# 缩放模板图像
scale_percent = 60  # 缩放比例为60%
width = int(template_image.shape[1] * scale_percent / 100)
height = int(template_image.shape[0] * scale_percent / 100)
dim = (width, height)
template_image_scaled = cv2.resize(template_image, dim, interpolation=cv2.INTER_AREA)

完整代码：

import cv2

source_image_name = "test_003.jpg"

threadhold = 0.88
#对该页面的所有区域进行匹配, 并且对template进行缩放 x 0.6
target_left, target_top = 1016, 222
target_right, target_bottom = 1280, 304

source_image = cv2.imread(source_image_name, cv2.IMREAD_COLOR)[target_top:target_bottom, target_left:target_right]
cv2.imshow('source_image',source_image)
cv2.waitKey(0)

# 先获得所有牌的模板

from os import listdir
from os.path import isfile, join

found_cards = []

onlyfiles = [f for f in listdir("cards_images") if isfile(join("cards_images", f)) and f.endswith(".jpg")]
for template_file_name in onlyfiles:

    template_image = cv2.imread(f"cards_images/{template_file_name}", cv2.IMREAD_COLOR)
    result = cv2.matchTemplate(source_image, template_image, cv2.TM_CCOEFF_NORMED)

    # 对template 缩放为 0.6
    scale_percent = 0.614
    width = int(template_image.shape[1] * scale_percent)
    height = int(template_image.shape[0] * scale_percent )
    template_image = cv2.resize( template_image, (width, height), interpolation=cv2.INTER_AREA)

    #template_image = cv2.imread(f"cards_images/{template_file_name}", cv2.IMREAD_GRAYSCALE)
    result = cv2.matchTemplate(source_image, template_image, cv2.TM_CCOEFF_NORMED)

    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
    print(f"== max_val: {max_val}")

    # 如果匹配到，则把对应的目标区域整个覆盖
    if max_val >= threadhold:
        #print(f"== 匹配到了, 牌：{template_file_name}, 准确率：{max_val}")
        top_left = max_loc
        # shape:  返回的是 height, width, channel.  所以 0 是高，1 是宽
        bottom_right = (top_left[0] + template_image.shape[1], top_left[1] + template_image.shape[0])
        cv2.rectangle(source_image, top_left, bottom_right, (0, 255, 0), -1)
        card_name = template_file_name.split(".")[0]
        found_cards.append(card_name)
        print(f"== 找到了：{card_name}, {max_val}")

        cv2.imshow('result', source_image)
        cv2.waitKey(0)
    else:
        pass

# 打印出所有的牌
print(f"== cards: {found_cards}")
short_names = []
for card_full_name in found_cards:
    short_names.append(card_full_name.split("_")[0])
print(f"== short name cards: {short_names}")

结果如下：