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picpac-image.h
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picpac-image.h
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#pragma once
#include <dlfcn.h>
#include <random>
#include <boost/core/noncopyable.hpp>
#include <opencv2/opencv.hpp>
#include "picpac.h"
#include "json.hpp"
#define PICPAC_CONFIG picpac::ImageStream::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,oversample);\
PICPAC_CONFIG_UPDATE(C,perturb);\
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_reset);\
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,annotate);
namespace picpac {
static inline int dtype_np2cv (string const &dt) {
if (dt == "uint8") {
return CV_8U;
}
else if (dt == "float32") {
return CV_32F;
}
else if (dt == "int32") {
return CV_32S;
}
else {
logging::error("dtype not supported.");
CHECK(false);
}
return 0;
}
using json = nlohmann::json;
struct RenderOptions {
int thickness;
int line_type;
int shift;
int point_radius;
bool show_numbers;
bool use_palette;
bool use_tag;
bool use_serial;
RenderOptions ()
: thickness(cv::FILLED),
line_type(8),
shift(0),
point_radius(5),
show_numbers(false),
use_palette(false),
use_tag(false),
use_serial(false)
{
}
};
// One annotation shape, box or polygon.
// Each shape must be controled by a series of control points
// geometric transformation of shape can be achieved by applying transformations
// to control points (with a callback function)
class Shape {
protected:
vector<cv::Point2f> controls;
public:
char const *type; // text name
cv::Scalar color;
float tag;
int serial;
cv::Scalar render_color (RenderOptions const &) const;
Shape (char const *t): type(t), color(1.0, 1.0, 1.0, 1.0), tag(0), serial(-1) {}
virtual ~Shape () {}
virtual std::unique_ptr<Shape> clone () const = 0;
virtual void transform (std::function<void(vector<cv::Point2f> *)> f) {
// some shape might need pre-post processing
f(&controls);
}
virtual void render (cv::Mat *, RenderOptions const &) const = 0;
static std::unique_ptr<Shape> create (json const &, cv::Size);
vector<cv::Point2f> const &__controls () const {
return controls;
};
};
struct Annotation: private boost::noncopyable {
cv::Size size;
vector<std::unique_ptr<Shape>> shapes;
Annotation (): size(0,0) {}
Annotation (char const *begin, char const *end, cv::Size);
bool empty () const { return size.width == 0;}
void clear () { size = cv::Size(0, 0); shapes.clear(); }
void transform (std::function<void(vector<cv::Point2f> *)> f) {
for (auto &s: shapes) {
s->transform(f);
}
}
void render (cv::Mat *m, RenderOptions const &opt) const {
for (auto &s: shapes) {
s->render(m, opt);
}
}
void copy (Annotation const &anno) {
size = anno.size;
shapes.clear();
for (auto &p: anno.shapes) {
shapes.emplace_back(p->clone());
}
}
void swap (Annotation &anno) {
std::swap(size, anno.size);
shapes.swap(anno.shapes);
}
};
// image with annotation
//
struct Facet {
enum {
IMAGE = 1,
LABEL = 2,
FEATURE = 3,
NONE = 4
};
int type;
cv::Mat image;
Annotation annotation;
Facet (): type(IMAGE) {}
Facet (char const *begin, char const *end, cv::Size sz): type(LABEL),
annotation(begin, end, sz) {
}
Facet (cv::Mat v, int type_ = IMAGE): type(type_), image(v) {
}
Facet (Facet &&ai) {
std::swap(type, ai.type);
cv::swap(image, ai.image);
annotation.swap(ai.annotation);
}
// ensure that all annotation has been cleared so data
// can be converted to python array
void check_pythonize () {
if (type == LABEL) {
if (!annotation.empty() || annotation.shapes.size()) {
logging::error("Your data contains a facet that cannot be converted to numpy array."
"This is because the label facet contains annotations that has not been"
" rasterized. This can be avoided by adding a 'drop' transformation to discard the annotation or "
" a 'rasterize' transformation.");
CHECK(0);
}
}
}
void operator = (Facet &&ai) {
std::swap(type, ai.type);
cv::swap(image, ai.image);
annotation.swap(ai.annotation);
}
cv::Size check_size () const {
cv::Size image_sz(0,0);
if (image.data) {
image_sz = image.size();
}
cv::Size anno_sz = annotation.size;
if (image_sz.width > 0 && anno_sz.width == 0) return image_sz;
if (image_sz.width == 0 && anno_sz.width > 0) return anno_sz;
if (image_sz.width > 0 && anno_sz.width > 0) {
CHECK(image_sz == anno_sz);
return image_sz;
}
if (image_sz.width == 0 && anno_sz.width ==0) {
return cv::Size(0, 0);
}
CHECK(0);
return image_sz;
}
private:
Facet (Facet &) = delete;
void operator = (Facet &) = delete;
};
struct Sample: private boost::noncopyable {
uint32_t id;
float label;
vector<string> raw;
vector<Facet> facets;
Sample () {}
void swap (Sample &v) {
std::swap(id, v.id);
std::swap(label, v.label);
raw.swap(v.raw);
facets.swap(v.facets);
}
void copy (Sample const &v) {
id = v.id;
label = v.label;
raw.clear();
for (auto const &s: v.raw) {
raw.push_back(s);
}
facets.clear();
facets.resize(v.facets.size());
for (unsigned i = 0; i < facets.size(); ++i) {
auto const &vi = v.facets[i];
facets[i].type = vi.type;
if (vi.image.data) {
facets[i].image = vi.image.clone();
}
facets[i].annotation.copy(vi.annotation);
}
}
Sample (Sample &&s) {
swap(s);
}
#if 0
private:
Sample (Sample &) = delete;
void operator = (Sample &) = delete;
#endif
};
class Transform {
public:
static std::unique_ptr<Transform> create (json const &);
virtual size_t pv_size () const {
return 0;
}
virtual size_t pv_sample (random_engine &rng, bool perturb, void *pv) const {
return 0;
}
virtual size_t apply (Sample *s, bool perturb, void const *pv) const {
size_t sz = pv_size();
for (auto &v: s->facets) {
size_t s = apply_one(&v, perturb, pv);
CHECK(s == sz);
}
return sz;
}
virtual size_t apply_one (Facet *, bool perturb, void const *) const {
return 0;
}
};
void load_transform_library (string const &path);
class Transforms: public Transform {
int total_pv_size;
vector<std::unique_ptr<Transform>> sub;
friend class SomeOf;
public:
Transforms (json const &spec): total_pv_size(0) {
for (auto it = spec.begin(); it != spec.end(); ++it) {
sub.emplace_back(Transform::create(*it));
total_pv_size += sub.back()->pv_size();
}
}
virtual size_t pv_size () const {
return total_pv_size;
}
virtual size_t pv_sample (random_engine &rng, bool perturb, void *pv) const {
char *p = reinterpret_cast<char *>(pv);
char *o = p;
for (unsigned i = 0; i < sub.size(); ++i) {
o += sub[i]->pv_sample(rng, perturb, o);
}
CHECK(total_pv_size == o-p);
return total_pv_size;
}
virtual size_t apply (Sample *s, bool perturb, void const *pv) const {
char const *p = reinterpret_cast<char const *>(pv);
char const *o = p;
for (unsigned i = 0; i < sub.size(); ++i) {
o += sub[i]->apply(s, perturb, o);
}
CHECK(total_pv_size == o-p);
return total_pv_size;
}
};
class ImageLoader {
public:
struct Config {
int channels; // -1: unchanged
int dtype;
vector<int> images;
vector<int> annotate;
vector<int> raw;
string transforms;
Config ()
: channels(-1), // unchanged
dtype(CV_32F),
images{0},
transforms("[]")
{
CHECK(channels == -1 || channels == 1 || channels == 3);
}
};
typedef Sample Value;
typedef Sample CacheValue;
struct PerturbVector {
string buffer;
};
ImageLoader (Config const &c)
: config(c), transforms(json::parse(c.transforms)), pv_size(transforms.pv_size())
{
}
void sample (random_engine &e, bool perturb, PerturbVector *p) const {
p->buffer.resize(pv_size);
transforms.pv_sample(e, perturb, &p->buffer[0]);
}
void load (RecordReader, bool perturb, PerturbVector const &, Value *,
CacheValue *c = nullptr, std::mutex *m = nullptr) const;
protected:
Config config;
Transforms transforms;
size_t pv_size;
};
typedef PrefetchStream<ImageLoader> ImageStream;
#if 0
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
};
protected:
cv::Scalar label_mean;//(0,0,0,0);
cv::Scalar mean;
unsigned onehot;
unsigned batch;
bool pad;
bool bgr2rgb;
int task;
public:
struct Config: public ImageStream::Config {
float mean_color1;
float mean_color2;
float mean_color3;
unsigned onehot;
unsigned batch;
bool pad;
bool bgr2rgb;
Config ():
mean_color1(0),
mean_color2(0),
mean_color3(0),
onehot(0), batch(1), pad(false), bgr2rgb(false) {
}
};
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) {
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 (order == ORDER_NCHW) {
images_shape->push_back(next.image.channels());
images_shape->push_back(next.image.rows);
images_shape->push_back(next.image.cols);
}
else if (order == ORDER_NHWC) {
images_shape->push_back(next.image.rows);
images_shape->push_back(next.image.cols);
images_shape->push_back(next.image.channels());
}
else CHECK(0);
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 (order == ORDER_NCHW) {
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 (order == ORDER_NCHW) {
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 (order == ORDER_NCHW) {
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 (order == ORDER_NCHW) {
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, order == ORDER_NCHW);
break;
default: CHECK(0);
}
++loaded;
}
}
catch (EoS const &) {
}
//if ((pad && (loaded == 0)) || ((!pad) && (loaded < batch))) throw EoS();
if (loaded == 0) throw EoS();
if (npad) *npad = batch - loaded;
}
};
#endif
cv::Mat decode_buffer (string_view, int mode = -1);
void encode_raw (cv::Mat, string *);
cv::Mat decode_raw (char const *, size_t);
class ImageEncoder {
protected:
string code;
vector<int> _params;
public:
ImageEncoder (string const &code_ = string()): code(code_) {
}
vector<int> ¶ms() { return _params; }
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);
void transcode (string const &input, 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);
}
static inline cv::Point round (cv::Point2f p) {
return cv::Point(std::round(p.x), std::round(p.y));
}
}