FFmpeg coverage

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