FFmpeg coverage


Directory: ../../../ffmpeg/
File: src/libavfilter/vf_dnn_classify.c
Date: 2024-04-19 07:31:02
Exec Total Coverage
Lines: 0 133 0.0%
Functions: 0 7 0.0%
Branches: 0 78 0.0%

Line Branch Exec Source
1 /*
2 * This file is part of FFmpeg.
3 *
4 * FFmpeg is free software; you can redistribute it and/or
5 * modify it under the terms of the GNU Lesser General Public
6 * License as published by the Free Software Foundation; either
7 * version 2.1 of the License, or (at your option) any later version.
8 *
9 * FFmpeg is distributed in the hope that it will be useful,
10 * but WITHOUT ANY WARRANTY; without even the implied warranty of
11 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
12 * Lesser General Public License for more details.
13 *
14 * You should have received a copy of the GNU Lesser General Public
15 * License along with FFmpeg; if not, write to the Free Software
16 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
17 */
18
19 /**
20 * @file
21 * implementing an classification filter using deep learning networks.
22 */
23
24 #include "libavutil/file_open.h"
25 #include "libavutil/mem.h"
26 #include "libavutil/opt.h"
27 #include "filters.h"
28 #include "dnn_filter_common.h"
29 #include "internal.h"
30 #include "video.h"
31 #include "libavutil/time.h"
32 #include "libavutil/avstring.h"
33 #include "libavutil/detection_bbox.h"
34
35 typedef struct DnnClassifyContext {
36 const AVClass *class;
37 DnnContext dnnctx;
38 float confidence;
39 char *labels_filename;
40 char *target;
41 char **labels;
42 int label_count;
43 } DnnClassifyContext;
44
45 #define OFFSET(x) offsetof(DnnClassifyContext, dnnctx.x)
46 #define OFFSET2(x) offsetof(DnnClassifyContext, x)
47 #define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
48 static const AVOption dnn_classify_options[] = {
49 { "dnn_backend", "DNN backend", OFFSET(backend_type), AV_OPT_TYPE_INT, { .i64 = DNN_OV }, INT_MIN, INT_MAX, FLAGS, .unit = "backend" },
50 #if (CONFIG_LIBOPENVINO == 1)
51 { "openvino", "openvino backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = DNN_OV }, 0, 0, FLAGS, .unit = "backend" },
52 #endif
53 DNN_COMMON_OPTIONS
54 { "confidence", "threshold of confidence", OFFSET2(confidence), AV_OPT_TYPE_FLOAT, { .dbl = 0.5 }, 0, 1, FLAGS},
55 { "labels", "path to labels file", OFFSET2(labels_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
56 { "target", "which one to be classified", OFFSET2(target), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
57 { NULL }
58 };
59
60 AVFILTER_DEFINE_CLASS(dnn_classify);
61
62 static int dnn_classify_post_proc(AVFrame *frame, DNNData *output, uint32_t bbox_index, AVFilterContext *filter_ctx)
63 {
64 DnnClassifyContext *ctx = filter_ctx->priv;
65 float conf_threshold = ctx->confidence;
66 AVDetectionBBoxHeader *header;
67 AVDetectionBBox *bbox;
68 float *classifications;
69 uint32_t label_id;
70 float confidence;
71 AVFrameSideData *sd;
72 int output_size = output->dims[3] * output->dims[2] * output->dims[1];
73 if (output_size <= 0) {
74 return -1;
75 }
76
77 sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
78 if (!sd) {
79 av_log(filter_ctx, AV_LOG_ERROR, "Cannot get side data in dnn_classify_post_proc\n");
80 return -1;
81 }
82 header = (AVDetectionBBoxHeader *)sd->data;
83
84 if (bbox_index == 0) {
85 av_strlcat(header->source, ", ", sizeof(header->source));
86 av_strlcat(header->source, ctx->dnnctx.model_filename, sizeof(header->source));
87 }
88
89 classifications = output->data;
90 label_id = 0;
91 confidence= classifications[0];
92 for (int i = 1; i < output_size; i++) {
93 if (classifications[i] > confidence) {
94 label_id = i;
95 confidence= classifications[i];
96 }
97 }
98
99 if (confidence < conf_threshold) {
100 return 0;
101 }
102
103 bbox = av_get_detection_bbox(header, bbox_index);
104 bbox->classify_confidences[bbox->classify_count] = av_make_q((int)(confidence * 10000), 10000);
105
106 if (ctx->labels && label_id < ctx->label_count) {
107 av_strlcpy(bbox->classify_labels[bbox->classify_count], ctx->labels[label_id], sizeof(bbox->classify_labels[bbox->classify_count]));
108 } else {
109 snprintf(bbox->classify_labels[bbox->classify_count], sizeof(bbox->classify_labels[bbox->classify_count]), "%d", label_id);
110 }
111
112 bbox->classify_count++;
113
114 return 0;
115 }
116
117 static void free_classify_labels(DnnClassifyContext *ctx)
118 {
119 for (int i = 0; i < ctx->label_count; i++) {
120 av_freep(&ctx->labels[i]);
121 }
122 ctx->label_count = 0;
123 av_freep(&ctx->labels);
124 }
125
126 static int read_classify_label_file(AVFilterContext *context)
127 {
128 int line_len;
129 FILE *file;
130 DnnClassifyContext *ctx = context->priv;
131
132 file = avpriv_fopen_utf8(ctx->labels_filename, "r");
133 if (!file){
134 av_log(context, AV_LOG_ERROR, "failed to open file %s\n", ctx->labels_filename);
135 return AVERROR(EINVAL);
136 }
137
138 while (!feof(file)) {
139 char *label;
140 char buf[256];
141 if (!fgets(buf, 256, file)) {
142 break;
143 }
144
145 line_len = strlen(buf);
146 while (line_len) {
147 int i = line_len - 1;
148 if (buf[i] == '\n' || buf[i] == '\r' || buf[i] == ' ') {
149 buf[i] = '\0';
150 line_len--;
151 } else {
152 break;
153 }
154 }
155
156 if (line_len == 0) // empty line
157 continue;
158
159 if (line_len >= AV_DETECTION_BBOX_LABEL_NAME_MAX_SIZE) {
160 av_log(context, AV_LOG_ERROR, "label %s too long\n", buf);
161 fclose(file);
162 return AVERROR(EINVAL);
163 }
164
165 label = av_strdup(buf);
166 if (!label) {
167 av_log(context, AV_LOG_ERROR, "failed to allocate memory for label %s\n", buf);
168 fclose(file);
169 return AVERROR(ENOMEM);
170 }
171
172 if (av_dynarray_add_nofree(&ctx->labels, &ctx->label_count, label) < 0) {
173 av_log(context, AV_LOG_ERROR, "failed to do av_dynarray_add\n");
174 fclose(file);
175 av_freep(&label);
176 return AVERROR(ENOMEM);
177 }
178 }
179
180 fclose(file);
181 return 0;
182 }
183
184 static av_cold int dnn_classify_init(AVFilterContext *context)
185 {
186 DnnClassifyContext *ctx = context->priv;
187 int ret = ff_dnn_init(&ctx->dnnctx, DFT_ANALYTICS_CLASSIFY, context);
188 if (ret < 0)
189 return ret;
190 ff_dnn_set_classify_post_proc(&ctx->dnnctx, dnn_classify_post_proc);
191
192 if (ctx->labels_filename) {
193 return read_classify_label_file(context);
194 }
195 return 0;
196 }
197
198 static const enum AVPixelFormat pix_fmts[] = {
199 AV_PIX_FMT_RGB24, AV_PIX_FMT_BGR24,
200 AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAYF32,
201 AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
202 AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
203 AV_PIX_FMT_NV12,
204 AV_PIX_FMT_NONE
205 };
206
207 static int dnn_classify_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
208 {
209 DnnClassifyContext *ctx = outlink->src->priv;
210 int ret;
211 DNNAsyncStatusType async_state;
212
213 ret = ff_dnn_flush(&ctx->dnnctx);
214 if (ret != 0) {
215 return -1;
216 }
217
218 do {
219 AVFrame *in_frame = NULL;
220 AVFrame *out_frame = NULL;
221 async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
222 if (async_state == DAST_SUCCESS) {
223 ret = ff_filter_frame(outlink, in_frame);
224 if (ret < 0)
225 return ret;
226 if (out_pts)
227 *out_pts = in_frame->pts + pts;
228 }
229 av_usleep(5000);
230 } while (async_state >= DAST_NOT_READY);
231
232 return 0;
233 }
234
235 static int dnn_classify_activate(AVFilterContext *filter_ctx)
236 {
237 AVFilterLink *inlink = filter_ctx->inputs[0];
238 AVFilterLink *outlink = filter_ctx->outputs[0];
239 DnnClassifyContext *ctx = filter_ctx->priv;
240 AVFrame *in = NULL;
241 int64_t pts;
242 int ret, status;
243 int got_frame = 0;
244 int async_state;
245
246 FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink);
247
248 do {
249 // drain all input frames
250 ret = ff_inlink_consume_frame(inlink, &in);
251 if (ret < 0)
252 return ret;
253 if (ret > 0) {
254 if (ff_dnn_execute_model_classification(&ctx->dnnctx, in, NULL, ctx->target) != 0) {
255 return AVERROR(EIO);
256 }
257 }
258 } while (ret > 0);
259
260 // drain all processed frames
261 do {
262 AVFrame *in_frame = NULL;
263 AVFrame *out_frame = NULL;
264 async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
265 if (async_state == DAST_SUCCESS) {
266 ret = ff_filter_frame(outlink, in_frame);
267 if (ret < 0)
268 return ret;
269 got_frame = 1;
270 }
271 } while (async_state == DAST_SUCCESS);
272
273 // if frame got, schedule to next filter
274 if (got_frame)
275 return 0;
276
277 if (ff_inlink_acknowledge_status(inlink, &status, &pts)) {
278 if (status == AVERROR_EOF) {
279 int64_t out_pts = pts;
280 ret = dnn_classify_flush_frame(outlink, pts, &out_pts);
281 ff_outlink_set_status(outlink, status, out_pts);
282 return ret;
283 }
284 }
285
286 FF_FILTER_FORWARD_WANTED(outlink, inlink);
287
288 return 0;
289 }
290
291 static av_cold void dnn_classify_uninit(AVFilterContext *context)
292 {
293 DnnClassifyContext *ctx = context->priv;
294 ff_dnn_uninit(&ctx->dnnctx);
295 free_classify_labels(ctx);
296 }
297
298 const AVFilter ff_vf_dnn_classify = {
299 .name = "dnn_classify",
300 .description = NULL_IF_CONFIG_SMALL("Apply DNN classify filter to the input."),
301 .priv_size = sizeof(DnnClassifyContext),
302 .init = dnn_classify_init,
303 .uninit = dnn_classify_uninit,
304 FILTER_INPUTS(ff_video_default_filterpad),
305 FILTER_OUTPUTS(ff_video_default_filterpad),
306 FILTER_PIXFMTS_ARRAY(pix_fmts),
307 .priv_class = &dnn_classify_class,
308 .activate = dnn_classify_activate,
309 };
310