decode_png.cpp

#include "decode_png.h"
#include "common_png.h"

namespace vision {
namespace image {

#if !PNG_FOUND
torch::Tensor decode_png(
    const torch::Tensor& data,
    ImageReadMode mode,
    bool allow_16_bits) {
  TORCH_CHECK(
      false, "decode_png: torchvision not compiled with libPNG support");
}
#else

bool is_little_endian() {
  uint32_t x = 1;
  return *(uint8_t*)&x;
}

torch::Tensor decode_png(
    const torch::Tensor& data,
    ImageReadMode mode,
    bool allow_16_bits) {
  // Check that the input tensor dtype is uint8
  TORCH_CHECK(data.dtype() == torch::kU8, "Expected a torch.uint8 tensor");
  // Check that the input tensor is 1-dimensional
  TORCH_CHECK(
      data.dim() == 1 && data.numel() > 0,
      "Expected a non empty 1-dimensional tensor");

  auto png_ptr =
      png_create_read_struct(PNG_LIBPNG_VER_STRING, nullptr, nullptr, nullptr);
  TORCH_CHECK(png_ptr, "libpng read structure allocation failed!")
  auto info_ptr = png_create_info_struct(png_ptr);
  if (!info_ptr) {
    png_destroy_read_struct(&png_ptr, nullptr, nullptr);
    // Seems redundant with the if statement. done here to avoid leaking memory.
    TORCH_CHECK(info_ptr, "libpng info structure allocation failed!")
  }

  auto datap = data.accessor<unsigned char, 1>().data();

  if (setjmp(png_jmpbuf(png_ptr)) != 0) {
    png_destroy_read_struct(&png_ptr, &info_ptr, nullptr);
    TORCH_CHECK(false, "Internal error.");
  }
  auto is_png = !png_sig_cmp(datap, 0, 8);
  TORCH_CHECK(is_png, "Content is not png!")

  struct Reader {
    png_const_bytep ptr;
  } reader;
  reader.ptr = png_const_bytep(datap) + 8;

  auto read_callback =
      [](png_structp png_ptr, png_bytep output, png_size_t bytes) {
        auto reader = static_cast<Reader*>(png_get_io_ptr(png_ptr));
        std::copy(reader->ptr, reader->ptr + bytes, output);
        reader->ptr += bytes;
      };
  png_set_sig_bytes(png_ptr, 8);
  png_set_read_fn(png_ptr, &reader, read_callback);
  png_read_info(png_ptr, info_ptr);

  png_uint_32 width, height;
  int bit_depth, color_type;
  int interlace_type;
  auto retval = png_get_IHDR(
      png_ptr,
      info_ptr,
      &width,
      &height,
      &bit_depth,
      &color_type,
      &interlace_type,
      nullptr,
      nullptr);

  if (retval != 1) {
    png_destroy_read_struct(&png_ptr, &info_ptr, nullptr);
    TORCH_CHECK(retval == 1, "Could read image metadata from content.")
  }

  auto max_bit_depth = allow_16_bits ? 16 : 8;
  auto err_msg = "At most " + std::to_string(max_bit_depth) +
      "-bit PNG images are supported currently.";
  if (bit_depth > max_bit_depth) {
    png_destroy_read_struct(&png_ptr, &info_ptr, nullptr);
    TORCH_CHECK(false, err_msg)
  }

  int channels = png_get_channels(png_ptr, info_ptr);

  if (color_type == PNG_COLOR_TYPE_GRAY && bit_depth < 8)
    png_set_expand_gray_1_2_4_to_8(png_ptr);

  int number_of_passes;
  if (interlace_type == PNG_INTERLACE_ADAM7) {
    number_of_passes = png_set_interlace_handling(png_ptr);
  } else {
    number_of_passes = 1;
  }

  if (mode != IMAGE_READ_MODE_UNCHANGED) {
    // TODO: consider supporting PNG_INFO_tRNS
    bool is_palette = (color_type & PNG_COLOR_MASK_PALETTE) != 0;
    bool has_color = (color_type & PNG_COLOR_MASK_COLOR) != 0;
    bool has_alpha = (color_type & PNG_COLOR_MASK_ALPHA) != 0;

    switch (mode) {
      case IMAGE_READ_MODE_GRAY:
        if (color_type != PNG_COLOR_TYPE_GRAY) {
          if (is_palette) {
            png_set_palette_to_rgb(png_ptr);
            has_alpha = true;
          }

          if (has_alpha) {
            png_set_strip_alpha(png_ptr);
          }

          if (has_color) {
            png_set_rgb_to_gray(png_ptr, 1, 0.2989, 0.587);
          }
          channels = 1;
        }
        break;
      case IMAGE_READ_MODE_GRAY_ALPHA:
        if (color_type != PNG_COLOR_TYPE_GRAY_ALPHA) {
          if (is_palette) {
            png_set_palette_to_rgb(png_ptr);
            has_alpha = true;
          }

          if (!has_alpha) {
            png_set_add_alpha(png_ptr, (1 << bit_depth) - 1, PNG_FILLER_AFTER);
          }

          if (has_color) {
            png_set_rgb_to_gray(png_ptr, 1, 0.2989, 0.587);
          }
          channels = 2;
        }
        break;
      case IMAGE_READ_MODE_RGB:
        if (color_type != PNG_COLOR_TYPE_RGB) {
          if (is_palette) {
            png_set_palette_to_rgb(png_ptr);
            has_alpha = true;
          } else if (!has_color) {
            png_set_gray_to_rgb(png_ptr);
          }

          if (has_alpha) {
            png_set_strip_alpha(png_ptr);
          }
          channels = 3;
        }
        break;
      case IMAGE_READ_MODE_RGB_ALPHA:
        if (color_type != PNG_COLOR_TYPE_RGB_ALPHA) {
          if (is_palette) {
            png_set_palette_to_rgb(png_ptr);
            has_alpha = true;
          } else if (!has_color) {
            png_set_gray_to_rgb(png_ptr);
          }

          if (!has_alpha) {
            png_set_add_alpha(png_ptr, (1 << bit_depth) - 1, PNG_FILLER_AFTER);
          }
          channels = 4;
        }
        break;
      default:
        png_destroy_read_struct(&png_ptr, &info_ptr, nullptr);
        TORCH_CHECK(false, "The provided mode is not supported for PNG files");
    }

    png_read_update_info(png_ptr, info_ptr);
  }

  auto num_pixels_per_row = width * channels;
  auto tensor = torch::empty(
      {int64_t(height), int64_t(width), channels},
      bit_depth <= 8 ? torch::kU8 : torch::kI32);

  if (bit_depth <= 8) {
    auto t_ptr = tensor.accessor<uint8_t, 3>().data();
    for (int pass = 0; pass < number_of_passes; pass++) {
      for (png_uint_32 i = 0; i < height; ++i) {
        png_read_row(png_ptr, t_ptr, nullptr);
        t_ptr += num_pixels_per_row;
      }
      t_ptr = tensor.accessor<uint8_t, 3>().data();
    }
  } else {
    // We're reading a 16bits png, but pytorch doesn't support uint16.
    // So we read each row in a 16bits tmp_buffer which we then cast into
    // a int32 tensor instead.
    if (is_little_endian()) {
      png_set_swap(png_ptr);
    }
    int32_t* t_ptr = tensor.accessor<int32_t, 3>().data();

    // We create a tensor instead of malloc-ing for automatic memory management
    auto tmp_buffer_tensor = torch::empty(
        {int64_t(num_pixels_per_row * sizeof(uint16_t))}, torch::kU8);
    uint16_t* tmp_buffer =
        (uint16_t*)tmp_buffer_tensor.accessor<uint8_t, 1>().data();

    for (int pass = 0; pass < number_of_passes; pass++) {
      for (png_uint_32 i = 0; i < height; ++i) {
        png_read_row(png_ptr, (uint8_t*)tmp_buffer, nullptr);
        // Now we copy the uint16 values into the int32 tensor.
        for (size_t j = 0; j < num_pixels_per_row; ++j) {
          t_ptr[j] = (int32_t)tmp_buffer[j];
        }
        t_ptr += num_pixels_per_row;
      }
      t_ptr = tensor.accessor<int32_t, 3>().data();
    }
  }
  png_destroy_read_struct(&png_ptr, &info_ptr, nullptr);
  return tensor.permute({2, 0, 1});
}
#endif

} // namespace image
} // namespace vision