1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 // See docs in ../ops/image_ops.cc 17 18 #include <memory> 19 #include "tensorflow/core/framework/op_kernel.h" 20 #include "tensorflow/core/framework/register_types.h" 21 #include "tensorflow/core/framework/tensor.h" 22 #include "tensorflow/core/framework/tensor_shape.h" 23 #include "tensorflow/core/framework/types.h" 24 #include "tensorflow/core/framework/types.pb.h" 25 #include "tensorflow/core/kernels/bounds_check.h" 26 #include "tensorflow/core/lib/core/status.h" 27 #include "tensorflow/core/platform/logging.h" 28 29 namespace tensorflow { 30 31 // Decode the contents of a BMP file 32 class DecodeBmpOp : public OpKernel { 33 public: 34 explicit DecodeBmpOp(OpKernelConstruction* context) : OpKernel(context) { 35 OP_REQUIRES_OK(context, context->GetAttr("channels", &channels_)); 36 OP_REQUIRES( 37 context, 38 channels_ == 0 || channels_ == 1 || channels_ == 3 || channels_ == 4, 39 errors::InvalidArgument("channels must be 0, 1, 3 or 4, got ", 40 channels_)); 41 } 42 inline int32 ByteSwapInt32ForBigEndian(int32 x) { 43 #if (__BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) 44 return le32toh(x); 45 #else 46 return x; 47 #endif 48 } 49 50 void Compute(OpKernelContext* context) override { 51 const Tensor& contents = context->input(0); 52 OP_REQUIRES(context, TensorShapeUtils::IsScalar(contents.shape()), 53 errors::InvalidArgument("contents must be scalar, got shape ", 54 contents.shape().DebugString())); 55 56 // Start decoding image to get shape details 57 const StringPiece input = contents.scalar<string>()(); 58 59 OP_REQUIRES(context, (32 <= input.size()), 60 errors::InvalidArgument("Incomplete bmp content, requires at " 61 "least 32 bytes to find the header " 62 "size, width, height, and bpp, got ", 63 input.size(), " bytes")); 64 65 const uint8* img_bytes = reinterpret_cast<const uint8*>(input.data()); 66 int32 header_size_ = internal::SubtleMustCopy( 67 *(reinterpret_cast<const int32*>(img_bytes + 10))); 68 const int32 header_size = ByteSwapInt32ForBigEndian(header_size_); 69 int32 width_ = internal::SubtleMustCopy( 70 *(reinterpret_cast<const int32*>(img_bytes + 18))); 71 const int32 width = ByteSwapInt32ForBigEndian(width_); 72 int32 height_ = internal::SubtleMustCopy( 73 *(reinterpret_cast<const int32*>(img_bytes + 22))); 74 const int32 height = ByteSwapInt32ForBigEndian(height_); 75 int32 bpp_ = internal::SubtleMustCopy( 76 *(reinterpret_cast<const int32*>(img_bytes + 28))); 77 const int32 bpp = ByteSwapInt32ForBigEndian(bpp_); 78 79 if (channels_) { 80 OP_REQUIRES(context, (channels_ == bpp / 8), 81 errors::InvalidArgument( 82 "channels attribute ", channels_, 83 " does not match bits per pixel from file ", bpp / 8)); 84 } else { 85 channels_ = bpp / 8; 86 } 87 88 // Current implementation only supports 1, 3 or 4 channel 89 // bitmaps. 90 OP_REQUIRES(context, (channels_ == 1 || channels_ == 3 || channels_ == 4), 91 errors::InvalidArgument( 92 "Number of channels must be 1, 3 or 4, was ", channels_)); 93 94 OP_REQUIRES(context, width > 0 && header_size >= 0, 95 errors::InvalidArgument("Width must be positive")); 96 OP_REQUIRES(context, header_size >= 0, 97 errors::InvalidArgument("header size must be nonnegative")); 98 99 // The real requirement is < 2^31 minus some headers and channel data, 100 // so rounding down to something that's still ridiculously big. 101 OP_REQUIRES( 102 context, 103 (static_cast<int64>(width) * std::abs(static_cast<int64>(height))) < 104 static_cast<int64>(std::numeric_limits<int32_t>::max() / 8), 105 errors::InvalidArgument( 106 "Total possible pixel bytes must be less than 2^30")); 107 108 const int32 abs_height = abs(height); 109 110 // there may be padding bytes when the width is not a multiple of 4 bytes 111 // 8 * channels == bits per pixel 112 const int row_size = (8 * channels_ * width + 31) / 32 * 4; 113 114 const int64 last_pixel_offset = static_cast<int64>(header_size) + 115 (abs_height - 1) * row_size + 116 (width - 1) * channels_; 117 118 // [expected file size] = [last pixel offset] + [last pixel size=channels] 119 const int64 expected_file_size = last_pixel_offset + channels_; 120 121 OP_REQUIRES( 122 context, (expected_file_size <= input.size()), 123 errors::InvalidArgument("Incomplete bmp content, requires at least ", 124 expected_file_size, " bytes, got ", 125 input.size(), " bytes")); 126 127 // if height is negative, data layout is top down 128 // otherwise, it's bottom up 129 bool top_down = (height < 0); 130 131 // Decode image, allocating tensor once the image size is known 132 Tensor* output = nullptr; 133 OP_REQUIRES_OK( 134 context, context->allocate_output( 135 0, TensorShape({abs_height, width, channels_}), &output)); 136 137 const uint8* bmp_pixels = &img_bytes[header_size]; 138 139 Decode(bmp_pixels, row_size, output->flat<uint8>().data(), width, 140 abs_height, channels_, top_down); 141 } 142 143 uint8* Decode(const uint8* input, const int row_size, uint8* const output, 144 const int width, const int height, const int channles, 145 bool top_down); 146 147 private: 148 int channels_; 149 }; 150 REGISTER_KERNEL_BUILDER(Name("DecodeBmp").Device(DEVICE_CPU), DecodeBmpOp); 151 152 uint8* DecodeBmpOp::Decode(const uint8* input, const int row_size, 153 uint8* const output, const int width, 154 const int height, const int channels, 155 bool top_down) { 156 for (int i = 0; i < height; i++) { 157 int src_pos; 158 int dst_pos; 159 160 for (int j = 0; j < width; j++) { 161 if (!top_down) { 162 src_pos = ((height - 1 - i) * row_size) + j * channels; 163 } else { 164 src_pos = i * row_size + j * channels; 165 } 166 167 dst_pos = (i * width + j) * channels; 168 169 switch (channels) { 170 case 1: 171 output[dst_pos] = input[src_pos]; 172 break; 173 case 3: 174 // BGR -> RGB 175 output[dst_pos] = input[src_pos + 2]; 176 output[dst_pos + 1] = input[src_pos + 1]; 177 output[dst_pos + 2] = input[src_pos]; 178 break; 179 case 4: 180 // BGRA -> RGBA 181 output[dst_pos] = input[src_pos + 2]; 182 output[dst_pos + 1] = input[src_pos + 1]; 183 output[dst_pos + 2] = input[src_pos]; 184 output[dst_pos + 3] = input[src_pos + 3]; 185 break; 186 default: 187 LOG(FATAL) << "Unexpected number of channels: " << channels; 188 break; 189 } 190 } 191 } 192 193 return output; 194 } 195 196 } // namespace tensorflow 197
