def find_template(self, im_source, im_search, threshold=None):
"""
模板匹配, 返回匹配度最高的坐标
:param im_source: 待匹配图像
:param im_search: 待匹配模板
:param threshold: 匹配度
:return: None or Rect
"""
start = time.time()
im_source, im_search = self.check_detection_input(im_source, im_search)
# 模板匹配取得res矩阵
res = self._get_template_result_matrix(im_source, im_search)
# 找到最佳匹配项
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
h, w = im_search.shape[:2]
# 求可信度
img_crop = im_search.adjustROI(max_loc[1], max_loc[1] + h, max_loc[0],
max_loc[0] + w)
confidence = self.cal_rgb_confidence(img_crop, im_search)
# confidence = self.test_cal_rgb_confidence(img_crop, im_search)
# 如果可信度小于threshold,则返回None
if confidence < (threshold or self.threshold):
return None
# 求取位置
x, y = max_loc
rect = Rect(x=x, y=y, width=w, height=h)
print('[tpl]{Rect}, confidence={confidence}, time={time:.2f}'.format(
confidence=confidence,
Rect=rect,
time=(time.time() - start) * 1000))
return generate_result(rect, confidence)
def find_template(im_source,
im_search,
threshold: float = 0.85,
mode=cv2.TM_CCOEFF_NORMED):
"""
模板匹配
:param im_source: 待匹配图像
:param im_search: 待匹配模板
:param threshold: 匹配度
:param mode: 识别模式
:return: None or Rect
"""
start = time.time()
im_source, im_search = check_detection_input(im_source, im_search)
# 模板匹配取得res矩阵
res = _get_template_result_matrix(im_source, im_search)
# 找到最佳匹配项
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
h, w = im_search.shape[:2]
# 求可信度
img_crop = im_source[max_loc[1]:max_loc[1] + h, max_loc[0]:max_loc[0] + w]
confidence = cal_rgb_confidence(img_crop, im_search)
# 如果可信度小于threshold,则返回None
if confidence < threshold:
return None
# 求取位置
x, y = max_loc
rect = Rect(x=x, y=y, width=w, height=h)
print('[tpl]{Rect}, confidence={confidence}, time={time:.2f}'.format(
confidence=confidence, Rect=rect, time=(time.time() - start) * 1000))
return generate_result(rect, confidence)
def find_templates(im_source,
im_search,
threshold: float = 0.85,
max_count=10):
"""
模板匹配
:param im_source: 待匹配图像
:param im_search: 待匹配模板
:param threshold: 匹配度
:param mode: 识别模式
:param max_count: 最多匹配数量
:return: None or Rect
"""
start = time.time()
im_source, im_search = check_detection_input(im_source, im_search)
# 模板匹配取得res矩阵
res = _get_template_result_matrix(im_source, im_search)
result = []
h, w = im_search.shape[:2]
while True:
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
img_crop = im_source[max_loc[1]:max_loc[1] + h,
max_loc[0]:max_loc[0] + w]
confidence = cal_rgb_confidence(img_crop, im_search)
if confidence < threshold or len(result) > max_count:
break
x, y = max_loc
rect = Rect(x=x, y=y, width=w, height=h)
result.append(generate_result(rect, confidence))
# 屏蔽最优结
cv2.rectangle(res, (int(max_loc[0] - w / 2), int(max_loc[1] - h / 2)),
(int(max_loc[0] + w / 2), int(max_loc[1] + h / 2)),
(0, 0, 0), -1)
if result:
print('[tpls] find counts:{counts}, time={time:.2f}ms{result}'.format(
counts=len(result),
time=(time.time() - start) * 1000,
result=''.join([
'\n\t{}, confidence={}'.format(x['rect'], x['confidence'])
for x in result
])))
return result if result else None
def find_sift(self, im_source, im_search, threshold: int = 0.8):
"""基于FlannBasedMatcher的SIFT实现"""
start_time = time.time()
im_source, im_search = check_detection_input(im_source, im_search)
# 第一步: 获取特征点集并匹配出特征点对 (最耗时的一段)
kp_sch, kp_src, good = self.get_key_points(im_search=im_search,
im_source=im_source)
# 第二步：根据匹配点对(good),提取出来识别区域:
rect = self.extract_good_points(im_source, im_search, kp_sch, kp_src,
good, threshold)
if not rect:
return None
# 第三步,通过识别矩阵周围+-1像素的矩阵,求出结果可信度，并且返回最大精准度的范围
confidences = []
similar_rect = create_similar_rect(rect.x, rect.y, rect.width,
rect.height)
pprint(similar_rect)
h, w = im_search.shape[:2]
for i in similar_rect:
# cv2.matchTemplate 目标和模板长宽不能一大一小
target_img = im_source[i.tl.y:i.br.y, i.tl.x:i.br.x]
print(i)
target_img = cv2.resize(target_img, (w, h))
confidences.append(
self._cal_sift_confidence(resize_img=target_img,
im_search=im_search))
# 提取最大值
if confidences:
confidence = max(confidences)
rect = similar_rect[confidences.index(confidence)]
else:
confidence = 0
best_match = generate_result(rect=rect, confi=confidence)
print(
'[sift]:{Rect}, confidence=(max={max_confidence:.5f},min={min_confidence:.5f}), time={time:.1f}ms'
.format(max_confidence=max(confidences),
min_confidence=min(confidences),
Rect=rect,
time=(time.time() - start_time) * 1000))
return best_match if confidence > threshold else None