def contain_image(self,
image_path: str = None,
image_object: np.ndarray = None,
threshold: float = None,
*args,
**kwargs):
assert image_path or image_object, 'should fill image_path or image_object'
if not threshold:
threshold = 0.99
if image_path:
logger.debug(f'found image path, use it first: {image_path}')
assert os.path.isfile(
image_path), f'image {image_path} not existed'
image_object = cv2.imread(image_path)
image_object = toolbox.turn_grey(image_object)
# TODO use client or itself..?
fi = FindIt(engine=['template'])
fi_template_name = 'default'
fi.load_template(fi_template_name, pic_object=image_object)
with toolbox.video_capture(self.video.path) as cap:
target_id = self.pick(*args, **kwargs)[0]
frame = toolbox.get_frame(cap, target_id)
frame = toolbox.turn_grey(frame)
result = fi.find(str(target_id), target_pic_object=frame)
find_result = result['data'][fi_template_name]['TemplateEngine']
position = find_result['target_point']
sim = find_result['target_sim']
logger.debug(f'position: {position}, sim: {sim}')
return sim > threshold
def contain_image(self,
image_path: str = None,
image_object: np.ndarray = None,
*args,
**kwargs) -> typing.Dict:
assert image_path or image_object, "should fill image_path or image_object"
if image_path:
logger.debug(f"found image path, use it first: {image_path}")
assert os.path.isfile(
image_path), f"image {image_path} not existed"
image_object = toolbox.imread(image_path)
image_object = toolbox.turn_grey(image_object)
# TODO use client or itself..?
fi = FindIt(engine=["template"])
fi_template_name = "default"
fi.load_template(fi_template_name, pic_object=image_object)
target_id = self.pick(*args, **kwargs)[0]
operator = self.video.get_operator()
frame = operator.get_frame_by_id(target_id)
result = fi.find(str(target_id), target_pic_object=frame.data)
return result["data"][fi_template_name]["TemplateEngine"]
class TemplateCompareHook(BaseHook):
def __init__(self, template_dict: typing.Dict[str, str], *args, **kwargs):
"""
args and kwargs will be sent to findit.__init__
:param template_dict:
# k: template name
# v: template picture path
:param args:
:param kwargs:
"""
super().__init__(*args, **kwargs)
self.fi = FindIt(*args, **kwargs)
self.template_dict = template_dict
@change_origin
def do(
self, frame_id: int, frame: np.ndarray, *_, **__
) -> typing.Optional[np.ndarray]:
super().do(frame_id, frame, *_, **__)
for each_template_name, each_template_path in self.template_dict.items():
self.fi.load_template(each_template_name, each_template_path)
res = self.fi.find(str(frame_id), target_pic_object=frame)
logger.debug(f"compare with template {self.template_dict}: {res}")
self.result[frame_id] = res
return
def contain_image(self,
image_path: str = None,
image_object: np.ndarray = None,
*args,
**kwargs) -> typing.Dict:
assert image_path or image_object, 'should fill image_path or image_object'
if image_path:
logger.debug(f'found image path, use it first: {image_path}')
assert os.path.isfile(
image_path), f'image {image_path} not existed'
image_object = toolbox.imread(image_path)
image_object = toolbox.turn_grey(image_object)
# TODO use client or itself..?
fi = FindIt(engine=['template'])
fi_template_name = 'default'
fi.load_template(fi_template_name, pic_object=image_object)
with toolbox.video_capture(self.video.path) as cap:
target_id = self.pick(*args, **kwargs)[0]
frame = toolbox.get_frame(cap, target_id)
frame = toolbox.turn_grey(frame)
result = fi.find(str(target_id), target_pic_object=frame)
return result['data'][fi_template_name]['TemplateEngine']
def analyse():
# required
# support multi pictures, split with ','
template_name = request.form.get("template_name")
template_name_list = template_name.split(",") if template_name else list()
template_dict = dict()
# optional
extra_str = request.form.get("extras")
extra_dict = json.loads(extra_str) if extra_str else dict()
new_extra_dict = utils.handle_extras(extra_dict)
for each_template_name in template_name_list:
template_path = utils.get_pic_path_by_name(each_template_name)
# file not existed
if not template_path:
return std_response(
status=STATUS_CLIENT_ERROR,
msg=f"no template named: {each_template_name}",
request=request.form,
response=dict(),
)
template_dict[each_template_name] = template_path
# save target pic
target_pic_file = request.files["file"]
temp_pic_file_object = tempfile.NamedTemporaryFile(mode="wb+",
suffix=".png",
delete=False)
temp_pic_file_object.write(target_pic_file.read())
temp_pic_file_object.close()
# init findit
fi = FindIt(need_log=True, **new_extra_dict)
# load all templates
for each_template_name, each_template_path in template_dict.items():
fi.load_template(each_template_name, pic_path=each_template_path)
_response = fi.find(
config.DEFAULT_TARGET_NAME,
target_pic_path=temp_pic_file_object.name,
**new_extra_dict,
)
# clean
os.remove(temp_pic_file_object.name)
return std_response(status=STATUS_OK,
msg="",
request=request.form,
response=_response)
def analyse():
# required
# support multi pictures, split with ','
template_name = request.form.get('template_name')
template_name_list = template_name.split(',')
template_dict = dict()
for each_template_name in template_name_list:
template_path = utils.get_pic_path_by_name(each_template_name)
# file not existed
if not template_path:
return std_response(
status=STATUS_CLIENT_ERROR,
msg='no template named {}'.format(each_template_name),
request=request.form,
response='',
)
template_dict[each_template_name] = template_path
# optional
extra_dict = json.loads(request.form.get('extras'))
new_extra_dict = utils.handle_extras(extra_dict)
# save target pic
target_pic_file = request.files['file']
temp_pic_file_object = tempfile.NamedTemporaryFile(mode='wb+',
suffix='.png',
delete=False)
temp_pic_file_object.write(target_pic_file.read())
temp_pic_file_object.close()
# init findit
fi = FindIt(need_log=True, **new_extra_dict)
# load all templates
for each_template_name, each_template_path in template_dict.items():
fi.load_template(each_template_name, pic_path=each_template_path)
_response = fi.find(config.DEFAULT_TARGET_NAME,
target_pic_path=temp_pic_file_object.name,
**new_extra_dict)
# clean
os.remove(temp_pic_file_object.name)
return std_response(
status=STATUS_OK,
msg='',
request=request.form,
response=_response,
)
def match_template_with_object(
template: np.ndarray,
target: np.ndarray,
engine_template_cv_method_name: str = None,
**kwargs,
) -> typing.Dict[str, typing.Any]:
# change the default method
if not engine_template_cv_method_name:
engine_template_cv_method_name = "cv2.TM_CCOEFF_NORMED"
fi = FindIt(
engine=["template"],
engine_template_cv_method_name=engine_template_cv_method_name,
**kwargs,
)
# load template
fi_template_name = "default"
fi.load_template(fi_template_name, pic_object=template)
result = fi.find(target_pic_name="", target_pic_object=target, **kwargs)
logger.debug(f"findit result: {result}")
return result["data"][fi_template_name]["TemplateEngine"]
import pprint
from findit import FindIt
fi = FindIt()
fi.load_template('wechat_logo', pic_path='pics/wechat_logo.png')
fi.load_template('app_store_logo', pic_path='pics/app_store_logo.png')
fi.load_template('music_logo', pic_path='pics/music_logo.png')
fi.load_template('album_logo', pic_path='pics/album_logo.png')
result = fi.find(
target_pic_name='screen',
target_pic_path='pics/screen.png',
)
pprint.pprint(result)
fi.load_template('wechat_logo', pic_path='pics/wechat_logo.png')
# 2. 或者直接加载通过cv2加载进来的图片
pic_object = cv2.imread('pics/app_store_logo.png')
# 传入的时候注意，传入的是一个列表，形式为 （名称，图片对象）
fi.load_template('app_store_logo', pic_object=pic_object)
# 在加载模板后即可开始分析
result = fi.find(
target_pic_name='screen',
# 目标图片可以直接传入路径
target_pic_path='pics/screen.png',
# 当然，也可以是cv2对象
# target_pic_object=some_object,
# 支持了蒙版图片的检测 （https://github.com/williamfzc/findit/issues/1）
# 同样的，你可以传入图片路径或对象
# mask_pic_path='wechat_logo.png',
# mask_pic_object=some_object,
# 如果你希望确认分析结果，你可以打开 mark_pic 开关
# 打开后，将会保存一张 标记了最终结果 的图片到本地，供参考用
# mark_pic=True,
)
# 在分析后，你可以通过 clear 重置所有模板。当然你也可以选择保留以进行其他分析。
fi.clear()
# sample result
time.sleep(1)
pprint.pprint(result)
# {'data': {'app_store_logo': {'FeatureEngine': (94.66734313964844,
from findit import FindIt
import cv2
import pprint
# new one
fi = FindIt()
# change config
fi.config.cv_method = cv2.TM_CCOEFF_NORMED
# load template picture
fi.load_template('./wechat_logo.png')
# and find it
# scale will resize template picture and auto-compare
# (1, 3, 10) means looping (1.0, 1.2, 1.4, 1.6, ... 2.6, 2.8, 3.0) times
result = fi.find('./wechat_screen.png', scale=(1, 3, 10))
# result looks like:
# {
# 'some desc': 'xxx',
# 'data': {
# 'template1.png': {
# 'position': (100, 500),
# 'sim': 0.66,
# }
# }
# }
pprint.pprint(result)