View Source Evision.WeChatQRCode.WeChatQRCode (Evision v0.2.9)
Summary
Functions
Both detects and decodes QR code. To simplify the usage, there is a only API: detectAndDecode
Both detects and decodes QR code. To simplify the usage, there is a only API: detectAndDecode
getScaleFactor
set scale factor QR code detector use neural network to detect QR. Before running the neural network, the input image is pre-processed by scaling. By default, the input image is scaled to an image with an area of 160000 pixels. The scale factor allows to use custom scale the input image: width = scaleFactorwidth height = scaleFactorwidth
Initialize the WeChatQRCode. It includes two models, which are packaged with caffe format. Therefore, there are prototxt and caffe models (In total, four paramenters).
Initialize the WeChatQRCode. It includes two models, which are packaged with caffe format. Therefore, there are prototxt and caffe models (In total, four paramenters).
Types
@type t() :: %Evision.WeChatQRCode.WeChatQRCode{ref: reference()}
Type that represents an WeChatQRCode.WeChatQRCode
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec detectAndDecode(t(), Evision.Mat.maybe_mat_in()) :: {[binary()], [Evision.Mat.t()]} | {:error, String.t()}
Both detects and decodes QR code. To simplify the usage, there is a only API: detectAndDecode
Positional Arguments
self:
Evision.WeChatQRCode.WeChatQRCode.t()
img:
Evision.Mat
.supports grayscale or color (BGR) image.
Return
retval:
[string]
points:
[Evision.Mat]
.optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
@return list of decoded string.
Python prototype (for reference only):
detectAndDecode(img[, points]) -> retval, points
@spec detectAndDecode(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {[binary()], [Evision.Mat.t()]} | {:error, String.t()}
Both detects and decodes QR code. To simplify the usage, there is a only API: detectAndDecode
Positional Arguments
self:
Evision.WeChatQRCode.WeChatQRCode.t()
img:
Evision.Mat
.supports grayscale or color (BGR) image.
Return
retval:
[string]
points:
[Evision.Mat]
.optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
@return list of decoded string.
Python prototype (for reference only):
detectAndDecode(img[, points]) -> retval, points
@spec getScaleFactor(Keyword.t()) :: any() | {:error, String.t()}
@spec getScaleFactor(t()) :: number() | {:error, String.t()}
getScaleFactor
Positional Arguments
- self:
Evision.WeChatQRCode.WeChatQRCode.t()
Return
- retval:
float
Python prototype (for reference only):
getScaleFactor() -> retval
set scale factor QR code detector use neural network to detect QR. Before running the neural network, the input image is pre-processed by scaling. By default, the input image is scaled to an image with an area of 160000 pixels. The scale factor allows to use custom scale the input image: width = scaleFactorwidth height = scaleFactorwidth
Positional Arguments
- self:
Evision.WeChatQRCode.WeChatQRCode.t()
- scalingFactor:
float
scaleFactor valuse must be > 0 and <= 1, otherwise the scaleFactor value is set to -1 and use default scaled to an image with an area of 160000 pixels.
Python prototype (for reference only):
setScaleFactor(_scalingFactor) -> None
Initialize the WeChatQRCode. It includes two models, which are packaged with caffe format. Therefore, there are prototxt and caffe models (In total, four paramenters).
Keyword Arguments
detector_prototxt_path:
string
.prototxt file path for the detector
detector_caffe_model_path:
string
.caffe model file path for the detector
super_resolution_prototxt_path:
string
.prototxt file path for the super resolution model
super_resolution_caffe_model_path:
string
.caffe file path for the super resolution model
Return
- self:
Evision.WeChatQRCode.WeChatQRCode.t()
Python prototype (for reference only):
WeChatQRCode([, detector_prototxt_path[, detector_caffe_model_path[, super_resolution_prototxt_path[, super_resolution_caffe_model_path]]]]) -> <wechat_qrcode_WeChatQRCode object>
@spec weChatQRCode(Keyword.t()) :: any() | {:error, String.t()}
@spec weChatQRCode( [ detector_caffe_model_path: term(), detector_prototxt_path: term(), super_resolution_caffe_model_path: term(), super_resolution_prototxt_path: term() ] | nil ) :: t() | {:error, String.t()}
Initialize the WeChatQRCode. It includes two models, which are packaged with caffe format. Therefore, there are prototxt and caffe models (In total, four paramenters).
Keyword Arguments
detector_prototxt_path:
string
.prototxt file path for the detector
detector_caffe_model_path:
string
.caffe model file path for the detector
super_resolution_prototxt_path:
string
.prototxt file path for the super resolution model
super_resolution_caffe_model_path:
string
.caffe file path for the super resolution model
Return
- self:
Evision.WeChatQRCode.WeChatQRCode.t()
Python prototype (for reference only):
WeChatQRCode([, detector_prototxt_path[, detector_caffe_model_path[, super_resolution_prototxt_path[, super_resolution_caffe_model_path]]]]) -> <wechat_qrcode_WeChatQRCode object>