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picpac-cv.h
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#pragma once
#include <random>
#include <opencv2/opencv.hpp>
#include <glog/logging.h>
#include "picpac.h"
#define PICPAC_CONFIG picpac::BatchImageStream::Config
#define PICPAC_CONFIG_UPDATE_ALL(C) \
PICPAC_CONFIG_UPDATE(C,seed);\
PICPAC_CONFIG_UPDATE(C,loop);\
PICPAC_CONFIG_UPDATE(C,shuffle);\
PICPAC_CONFIG_UPDATE(C,reshuffle);\
PICPAC_CONFIG_UPDATE(C,stratify);\
PICPAC_CONFIG_UPDATE(C,split);\
PICPAC_CONFIG_UPDATE(C,split_fold);\
PICPAC_CONFIG_UPDATE(C,split_negate);\
PICPAC_CONFIG_UPDATE(C,mixin);\
PICPAC_CONFIG_UPDATE(C,mixin_group_delta);\
PICPAC_CONFIG_UPDATE(C,mixin_max);\
PICPAC_CONFIG_UPDATE(C,cache);\
PICPAC_CONFIG_UPDATE(C,preload);\
PICPAC_CONFIG_UPDATE(C,threads);\
PICPAC_CONFIG_UPDATE(C,channels);\
PICPAC_CONFIG_UPDATE(C,min_size);\
PICPAC_CONFIG_UPDATE(C,max_size);\
PICPAC_CONFIG_UPDATE(C,resize_width);\
PICPAC_CONFIG_UPDATE(C,resize_height);\
PICPAC_CONFIG_UPDATE(C,crop_width);\
PICPAC_CONFIG_UPDATE(C,crop_height);\
PICPAC_CONFIG_UPDATE(C,decode_mode);\
PICPAC_CONFIG_UPDATE(C,annotate);\
PICPAC_CONFIG_UPDATE(C,anno_type);\
PICPAC_CONFIG_UPDATE(C,anno_copy);\
PICPAC_CONFIG_UPDATE(C,anno_color1); \
PICPAC_CONFIG_UPDATE(C,anno_color2); \
PICPAC_CONFIG_UPDATE(C,anno_color3); \
PICPAC_CONFIG_UPDATE(C,anno_thickness);\
PICPAC_CONFIG_UPDATE(C,anno_min_ratio); \
PICPAC_CONFIG_UPDATE(C,perturb);\
PICPAC_CONFIG_UPDATE(C,pert_colorspace); \
PICPAC_CONFIG_UPDATE(C,pert_color1); \
PICPAC_CONFIG_UPDATE(C,pert_color2); \
PICPAC_CONFIG_UPDATE(C,pert_color3); \
PICPAC_CONFIG_UPDATE(C,pert_angle); \
PICPAC_CONFIG_UPDATE(C,pert_min_scale); \
PICPAC_CONFIG_UPDATE(C,pert_max_scale); \
PICPAC_CONFIG_UPDATE(C,pert_hflip); \
PICPAC_CONFIG_UPDATE(C,pert_vflip); \
PICPAC_CONFIG_UPDATE(C,mean_color1); \
PICPAC_CONFIG_UPDATE(C,mean_color2); \
PICPAC_CONFIG_UPDATE(C,mean_color3); \
PICPAC_CONFIG_UPDATE(C,onehot);\
PICPAC_CONFIG_UPDATE(C,batch);\
PICPAC_CONFIG_UPDATE(C,pad);\
PICPAC_CONFIG_UPDATE(C,bgr2rgb);\
PICPAC_CONFIG_UPDATE(C,channel_first);
namespace json11 {
class Json;
}
namespace picpac {
class ImageLoader {
public:
enum {
ANNOTATE_NONE = 0,
ANNOTATE_IMAGE = 1,
ANNOTATE_JSON = 2,
ANNOTATE_AUTO = 3 // for autoencoder, use input as annotation
};
enum {
COLOR_DEFAULT = 0,
COLOR_HSV = 1,
COLOR_Lab = 2
};
struct Config {
int channels; // -1: unchanged
int min_size;
int max_size;
int resize_width;
int resize_height;
int crop_width;
int crop_height;
int decode_mode; // image load mode
string annotate;
int anno_type; // annotate image opencv type
bool anno_copy; // copy input image first for visualization
float anno_color1;
float anno_color2;
float anno_color3;
int anno_thickness;
float anno_min_ratio;
// -1 to fill (opencv rule)
// perturbation
bool perturb;
// perturbation output retains input image size
string pert_colorspace;
float pert_color1;
float pert_color2;
float pert_color3;
// perturb color range
float pert_angle;
// perturb angle range
float pert_min_scale;
float pert_max_scale;
bool pert_hflip, pert_vflip;
float pert_border;
Config ()
: channels(0),
min_size(-1),
max_size(-1),
resize_width(-1),
resize_height(-1),
crop_width(-1),
crop_height(-1),
decode_mode(cv::IMREAD_UNCHANGED),
anno_type(CV_8UC1),
anno_copy(false),
anno_color1(1),
anno_color2(0),
anno_color3(0),
anno_thickness(CV_FILLED),
anno_min_ratio(0),
perturb(false),
pert_color1(0),
pert_color2(0),
pert_color3(0),
pert_angle(0),
pert_min_scale(1),
pert_max_scale(1),
pert_hflip(false),
pert_vflip(false),
pert_border(cv::BORDER_CONSTANT)
{
}
};
struct Value {
float label;
cv::Mat image;
cv::Mat annotation;
};
typedef Value CacheValue;
struct PerturbVector {
cv::Scalar color;
float angle, scale;
bool hflip, vflip;
int shiftx, shifty;
};
ImageLoader (Config const &c)
: config(c),
annotate(ANNOTATE_NONE),
delta_color1(-c.pert_color1, c.pert_color1),
delta_color2(-c.pert_color2, c.pert_color2),
delta_color3(-c.pert_color3, c.pert_color3),
linear_angle(-c.pert_angle, c.pert_angle),
linear_scale(c.pert_min_scale, c.pert_max_scale)
{
if (config.annotate == "json") {
annotate = ANNOTATE_JSON;
}
else if (config.annotate == "image") {
annotate = ANNOTATE_IMAGE;
}
else if (config.annotate == "auto") {
annotate = ANNOTATE_AUTO;
}
if (config.pert_colorspace == "Lab") {
colorspace = COLOR_Lab;
}
else if (config.pert_colorspace == "HSV") {
colorspace = COLOR_HSV;
}
else {
colorspace = COLOR_DEFAULT;
}
}
template <typename RNG>
void sample (RNG &e, PerturbVector *p) {
if (config.perturb) {
p->hflip = bool(e() % 2) && config.pert_hflip;
p->vflip = bool(e() % 2) && config.pert_vflip;
p->color[0] = delta_color1(e);
p->color[1] = delta_color2(e);
p->color[2] = delta_color3(e);
p->angle = linear_angle(e);
p->scale = linear_scale(e);
p->shiftx = e();
p->shifty = e();
}
}
void load (RecordReader, PerturbVector const &, Value *,
CacheValue *c = nullptr, std::mutex *m = nullptr) const;
private:
Config config;
int annotate;
int colorspace;
std::uniform_int_distribution<int> delta_color1; //(min_R, max_R);
std::uniform_int_distribution<int> delta_color2; //(min_R, max_R);
std::uniform_int_distribution<int> delta_color3; //(min_R, max_R);
std::uniform_real_distribution<float> linear_angle;
std::uniform_real_distribution<float> linear_scale;
};
typedef PrefetchStream<ImageLoader> ImageStream;
namespace impl {
template <typename Tfrom = uint8_t, typename Tto = float>
Tto *split_helper (cv::Mat image, Tto *buffer, cv::Scalar mean, bool bgr2rgb) {
Tto *ptr_b = buffer;
Tto *ptr_g = buffer;
Tto *ptr_r = buffer;
if (bgr2rgb) {
CHECK(image.channels() == 3);
ptr_g += image.total();
ptr_b += 2 * image.total();
}
else if (image.channels() == 2) {
ptr_g += image.total(); // g, r
}
else if (image.channels() == 3) {
ptr_g += image.total(); // b, g, r
ptr_r += 2 * image.total();
}
unsigned off = 0;
for (int i = 0; i < image.rows; ++i) {
Tfrom const *line = image.ptr<Tfrom const>(i);
for (int j = 0; j < image.cols; ++j) {
ptr_b[off] = (*line++) - mean[0];
if (image.channels() > 1) {
ptr_g[off] = (*line++) - mean[1];
}
if (image.channels() > 2) {
ptr_r[off] = (*line++) - mean[2];
}
++off;
}
}
CHECK(off == image.total());
return buffer + image.channels() * image.total();
}
template <typename Tto = float>
Tto *split_copy (cv::Mat image, Tto *buffer, cv::Scalar mean, bool bgr2rgb) {
int depth = image.depth();
int ch = image.channels();
CHECK((ch >= 1) && (ch <= 3));
switch (depth) {
case CV_8U: return split_helper<uint8_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_8S: return split_helper<int8_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_16U: return split_helper<uint16_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_16S: return split_helper<int16_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_32S: return split_helper<int32_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_32F: return split_helper<float, Tto>(image, buffer, mean, bgr2rgb);
case CV_64F: return split_helper<double, Tto>(image, buffer, mean, bgr2rgb);
}
CHECK(0) << "Mat type not supported.";
return nullptr;
}
template <typename Tfrom = uint8_t, typename Tto = float>
Tto *copy_helper (cv::Mat image, Tto *buffer, cv::Scalar mean, bool bgr2rgb) {
CHECK(!bgr2rgb);
unsigned off = 0;
for (int i = 0; i < image.rows; ++i) {
Tfrom const *line = image.ptr<Tfrom const>(i);
for (int j = 0; j < image.cols; ++j) {
buffer[off++] = (*line++) - mean[0];
if (image.channels() > 1) {
buffer[off++] = (*line++) - mean[1];
}
if (image.channels() > 2) {
buffer[off++] = (*line++) - mean[2];
}
}
}
CHECK(off == image.total() * image.channels());
return buffer + image.channels() * image.total();
}
template <typename Tto = float>
Tto *copy (cv::Mat image, Tto *buffer, cv::Scalar mean, bool bgr2rgb) {
CHECK(!bgr2rgb) << "Not supported";
int depth = image.depth();
int ch = image.channels();
CHECK((ch >= 1) && (ch <= 3));
switch (depth) {
case CV_8U: return copy_helper<uint8_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_8S: return copy_helper<int8_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_16U: return copy_helper<uint16_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_16S: return copy_helper<int16_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_32S: return copy_helper<int32_t, Tto>(image, buffer, mean, bgr2rgb);
case CV_32F: return copy_helper<float, Tto>(image, buffer, mean, bgr2rgb);
case CV_64F: return copy_helper<double, Tto>(image, buffer, mean, bgr2rgb);
}
CHECK(0) << "Mat type not supported.";
return nullptr;
}
template <typename Tfrom, typename Tto = float>
Tto *onehot_helper (cv::Mat image, Tto *buffer, unsigned onehot, bool cf) {
size_t ch_size = image.total();
size_t total_size = ch_size * onehot;
Tto *buffer_end = buffer + total_size;
std::fill(buffer, buffer_end, 0);
int off = 0;
if (cf) { // channel comes first
for (int i = 0; i < image.rows; ++i) {
Tfrom const *row = image.ptr<Tfrom const>(i);
for (int j = 0; j < image.cols; ++j) {
Tfrom v = row[j];
unsigned c(v);
CHECK(c == v);
CHECK(c <= MAX_CATEGORIES);
if (c < ch_size) {
Tto *plane = buffer + c * ch_size;
plane[off] = 1;
}
++off;
}
}
}
else { // channel comes last
Tto *o = buffer;
for (int i = 0; i < image.rows; ++i) {
Tfrom const *row = image.ptr<Tfrom const>(i);
for (int j = 0; j < image.cols; ++j) {
Tfrom v = row[j];
unsigned c(v);
CHECK(c == v);
CHECK(c <= MAX_CATEGORIES);
if (c < ch_size) {
o[c] = 1;
}
o += ch_size;
}
}
}
return buffer_end;
}
template <typename Tto = float>
Tto *onehot_encode (cv::Mat image, Tto *buffer, unsigned onehot, bool cf) {
int depth = image.depth();
int ch = image.channels();
CHECK(ch == 1);
switch (depth) {
case CV_8U: return onehot_helper<uint8_t, Tto>(image, buffer, onehot, cf);
case CV_8S: return onehot_helper<int8_t, Tto>(image, buffer, onehot, cf);
case CV_16U: return onehot_helper<uint16_t, Tto>(image, buffer, onehot, cf);
case CV_16S: return onehot_helper<int16_t, Tto>(image, buffer, onehot, cf);
case CV_32S: return onehot_helper<int32_t, Tto>(image, buffer, onehot, cf);
case CV_32F: return onehot_helper<float, Tto>(image, buffer, onehot, cf);
case CV_64F: return onehot_helper<double, Tto>(image, buffer, onehot, cf);
}
CHECK(0) << "Mat type not supported.";
return nullptr;
}
}
// this is the main interface for most of the
// deep learning libraries.
class BatchImageStream: public ImageStream {
public:
enum {
TASK_REGRESSION = 1,
TASK_CLASSIFICATION = 2,
TASK_PIXEL_REGRESSION = 3,
TASK_PIXEL_CLASSIFICATION = 4
};
private:
cv::Scalar label_mean;//(0,0,0,0);
cv::Scalar mean;
unsigned onehot;
unsigned batch;
bool pad;
bool bgr2rgb;
int task;
bool channel_first;
public:
struct Config: public ImageStream::Config {
float mean_color1;
float mean_color2;
float mean_color3;
unsigned onehot;
unsigned batch;
bool pad;
bool bgr2rgb;
bool channel_first;
Config ():
mean_color1(0),
mean_color2(0),
mean_color3(0),
onehot(0), batch(1), pad(false), bgr2rgb(false), channel_first(true) {
}
};
BatchImageStream (fs::path const &path, Config const &c)
: ImageStream(path, c),
label_mean(0,0,0,0),
mean(cv::Scalar(c.mean_color1, c.mean_color2, c.mean_color3)),
onehot(c.onehot),
batch(c.batch), pad(c.pad), bgr2rgb(c.bgr2rgb), channel_first(c.channel_first) {
ImageStream::Value &v(ImageStream::peek());
if (!v.annotation.data) {
if (onehot > 0) {
task = TASK_CLASSIFICATION;
}
else {
task = TASK_REGRESSION;
}
}
else {
if (onehot) {
CHECK(v.annotation.channels() == 1);
task = TASK_PIXEL_CLASSIFICATION;
}
else {
task = TASK_PIXEL_REGRESSION;
if (c.annotate == "auto") {
label_mean = mean;
}
}
}
}
template <typename T=unsigned>
void next_shape (vector<T> *images_shape,
vector<T> *labels_shape) {
Value &next = ImageStream::peek();
images_shape->clear();
images_shape->push_back(batch);
if (channel_first) {
images_shape->push_back(next.image.channels());
images_shape->push_back(next.image.rows);
images_shape->push_back(next.image.cols);
}
else {
images_shape->push_back(next.image.rows);
images_shape->push_back(next.image.cols);
images_shape->push_back(next.image.channels());
}
labels_shape->clear();
labels_shape->push_back(batch);
switch (task) {
case TASK_REGRESSION:
CHECK(!next.annotation.data);
break;
case TASK_CLASSIFICATION:
CHECK(!next.annotation.data);
labels_shape->push_back(onehot); break;
case TASK_PIXEL_REGRESSION:
CHECK(next.annotation.data);
if (channel_first) {
labels_shape->push_back(next.annotation.channels());
labels_shape->push_back(next.annotation.rows);
labels_shape->push_back(next.annotation.cols);
}
else {
labels_shape->push_back(next.annotation.rows);
labels_shape->push_back(next.annotation.cols);
labels_shape->push_back(next.annotation.channels());
}
break;
case TASK_PIXEL_CLASSIFICATION:
CHECK(next.annotation.data);
CHECK(next.annotation.channels() == 1);
if (channel_first) {
labels_shape->push_back(onehot);
labels_shape->push_back(next.annotation.rows);
labels_shape->push_back(next.annotation.cols);
}
else {
labels_shape->push_back(next.annotation.rows);
labels_shape->push_back(next.annotation.cols);
labels_shape->push_back(onehot);
}
break;
default: CHECK(0);
}
}
template <typename T1=float, typename T2=float>
void next_fill (T1 *images, T2 *labels, unsigned *npad = nullptr) {
vector<unsigned> ishape, lshape;
vector<unsigned> ishape2, lshape2;
unsigned loaded = 0;
try {
for (unsigned i = 0; i < batch; ++i) {
if (i) {
next_shape(&ishape2, &lshape2);
CHECK(ishape == ishape2);
CHECK(lshape == lshape2);
}
else {
next_shape(&ishape, &lshape);
}
Value v(next());
if (channel_first) {
images = impl::split_copy<T1>(v.image, images, mean, bgr2rgb);
}
else {
images = impl::copy<T1>(v.image, images, mean, bgr2rgb);
}
switch (task) {
case TASK_REGRESSION:
*labels = v.label;
++labels;
break;
case TASK_CLASSIFICATION:
{
unsigned c = unsigned(v.label);
CHECK(c == v.label) << "float label for classification";
CHECK(c <= MAX_CATEGORIES);
std::fill(labels, labels + onehot, 0);
labels[c] = 1;
labels += onehot;
}
break;
case TASK_PIXEL_REGRESSION:
if (channel_first) {
labels = impl::split_copy<T2>(v.annotation, labels, label_mean, bgr2rgb);
}
else {
labels = impl::copy<T2>(v.annotation, labels, label_mean, bgr2rgb);
}
break;
case TASK_PIXEL_CLASSIFICATION:
labels = impl::onehot_encode<T2>(v.annotation, labels, onehot, channel_first);
break;
default: CHECK(0);
}
++loaded;
}
}
catch (EoS const &) {
}
if ((pad && (loaded == 0)) || ((!pad) && (loaded < batch))) throw EoS();
if (npad) *npad = batch - loaded;
}
};
cv::Mat decode_buffer (const_buffer, int mode = -1);
void encode_raw (cv::Mat, string *);
cv::Mat decode_raw (char const *, size_t);
class ImageEncoder {
protected:
string code;
public:
ImageEncoder (string const &code_ = string()): code(code_) {
}
void encode (cv::Mat const &image, string *);
};
class ImageReader: public ImageEncoder {
int max;
int resize;
int mode;
public:
ImageReader (int max_ = 800, int resize_ = -1, int mode_ = cv::IMREAD_UNCHANGED, string const &code_ = string())
: ImageEncoder(code_), max(max_), resize(resize_), mode(mode_) {
}
void read (fs::path const &path, string *data);
};
float LimitSize (cv::Mat input, int min_size, int max_size, cv::Mat *output);
static inline float LimitSize (cv::Mat input, int max_size, cv::Mat *output) {
return LimitSize(input, -1, max_size, output);
}
class Shape {
string _type;
public:
Shape (char const *t): _type(t) {}
virtual ~Shape () {}
virtual void draw (cv::Mat *, cv::Scalar v, int thickness = CV_FILLED) const = 0;
virtual void bbox (cv::Rect_<float> *) const = 0;
virtual void zoom (cv::Rect_<float> const &) = 0;
virtual void dump (json11::Json *) const = 0;
string const &type () const {
return _type;
}
virtual std::shared_ptr<Shape> clone () const = 0;
static std::shared_ptr<Shape> create (json11::Json const &geo);
};
struct Annotation {
vector<std::shared_ptr<Shape>> shapes;
Annotation () {}
Annotation (string const &txt);
void dump (string *) const;
void draw (cv::Mat *m, cv::Scalar v, int thickness = -1) const;
void bbox (cv::Rect_<float> *bb) const;
void zoom (cv::Rect_<float> const &bb);
};
}