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修改骨架识别，让骨架识别能够区分出各个部位。
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-19
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4 files changed

+167
-19
lines changed

‎.idea/modules.xml

-3
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‎README.md

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Original file line numberDiff line numberDiff line change
@@ -26,6 +26,7 @@ TensorFlowImageClassifier2 为车道检测之后不规则绘制（因时间仓
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TensorFlowImageClassifier3 是用来识别人体骨架的 这个模型是有特定输入和特定输出的 需要经过3层转换 才能使用
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接下来准备上线道路障碍物识别...
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最新版骨架识别目前支持区分各个身体部位具体情况请看注释
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Camera2BasicFragment4
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这是一个用检测来识别车道和前车

‎app/src/main/java/com/tfcamerademo/model/TensorFlowImageClassifier3.java

+166-16
Original file line numberDiff line numberDiff line change
@@ -194,6 +194,7 @@ public List<Recognition> recognizeImage(final Bitmap bitmap) {
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int[] b = new int[46 * 46 * 57];
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//取出第一个通道色彩值
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float[][] floats5 = new float[(outputs.length / 57) * 19][19];
197+
//转成矩阵把小于0.1的值归0
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for (int i = 0, sum = -1; i < outputs.length; i++) {
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if (0 <= i % 57 && i % 57 < 19) {
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if (0 == i % 57)
@@ -207,32 +208,181 @@ public List<Recognition> recognizeImage(final Bitmap bitmap) {
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for (int i = 0; i < floats5.length; i++) {
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System.arraycopy(floats5[i], 0, floats2[i], 0, 18);
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}
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//取出每18个点中的最大的点
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float max = 0;
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float[] floats3 = new float[(outputs.length / 57) * 19];
212-
for (int i = 0; i < floats2.length; i++) {
215+
for (int i = 0, sum = -1; i < floats2.length; i++) {
213216
for (int j = 0; j < floats2[i].length; j++) {
214-
if (floats2[i][j] > max)
215-
max = floats2[i][j];
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sum++;
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if (floats2[i][j] > max) {
219+
//这个是设置识别准确度多少以上保留数值越大越精确 同样识别出来的东西越少
220+
if (floats2[i][j] > 0.3f)
221+
switch (sum % 18) {
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case 0:
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//鼻子
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//这里写20的原因是因为 0太多了 如果写0的话 就会和空白处重复
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max = 19;
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break;
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case 1:
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//脖子
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max = 1;
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break;
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case 2:
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//右上臂
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max = 2;
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break;
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case 3:
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//右肘关节
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max = 3;
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break;
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case 4:
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//右手腕关节
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max = 4;
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break;
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case 5:
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//左肩
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max = 5;
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break;
247+
case 6:
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//左肘关节
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max = 6;
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break;
251+
case 7:
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//左手腕关节
253+
max = 7;
254+
break;
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case 8:
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//右臀部
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max = 8;
258+
break;
259+
case 9:
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//右膝关节
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max = 9;
262+
break;
263+
case 10:
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//右裸关节
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max = 10;
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break;
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case 11:
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//左臀部
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max = 11;
270+
break;
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case 12:
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//左膝关节
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max = 12;
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break;
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case 13:
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//左裸关节
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max = 13;
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break;
279+
case 14:
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//右眼
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max = 14;
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break;
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case 15:
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//左眼
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max = 15;
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break;
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case 16:
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//右耳
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max = 16;
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break;
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case 17:
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//左耳
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max = 17;
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break;
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case 18:
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//背景（因为背景没有识别值 所以自动过滤掉了）
297+
max = 18;
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break;
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}
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}
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}
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floats3[i] = max;
218303
max = 0;
219304
}
220305

221-
//取出每18个点中最大的点
222-
float[] floats4 = new float[((outputs.length / 57) * 19) * 18];
223-
for (int i = 0, sum = 0; i < floats2.length; i++) {
224-
for (int j = 0; j < floats2[i].length; j++) {
225-
floats4[sum] = floats2[i][j];
226-
sum++;
227-
}
228-
}
229-
230306
//将点颜色显示出来
231307
for (int j = 0; j < floats3.length; j++) {
232-
//这个是设置识别准确度多少以上保留数值越大越精确 同样识别出来的东西越少
233-
if (floats3[j] > 0.3f) {
234-
//识别准确度大于0.3f则显示为绿色
235-
b[j] = 0xff << 24 | 0xff << 8;
308+
//以下为20种颜色值（不保证是否重复，不同颜色值的意义是 告诉大家这是不同部位）
309+
switch ((int) floats3[j]) {
310+
case 1:
311+
//脖子
312+
b[j] = 0xff << 24 | 0xff << 8;
313+
break;
314+
case 2:
315+
//右肩
316+
b[j] = 0xff << 24 | 0xff;
317+
break;
318+
case 3:
319+
//右肘关节
320+
b[j] = 0xff << 24 | 0xff << 8 | 0xff;
321+
break;
322+
case 4:
323+
//右手腕关节
324+
b[j] = 0xff << 24 | 0xff << 16 | 0xff;
325+
break;
326+
case 5:
327+
//左肩
328+
b[j] = 0xff << 24 | 0xff << 16 | 0xff << 8;
329+
break;
330+
case 6:
331+
//左肘关节
332+
b[j] = 0xff << 24 | 0xff << 16 | 0x0f << 8;
333+
break;
334+
case 7:
335+
//左手腕关节
336+
b[j] = 0xff << 24 | 0x0f << 16 | 0x0f << 8;
337+
break;
338+
case 8:
339+
//右臀部
340+
b[j] = 0xff << 24 | 0x0f << 16 | 0x0f << 8 | 0xff;
341+
break;
342+
case 9:
343+
//右膝关节
344+
b[j] = 0xff << 24 | 0xff << 16 | 0x0f << 8 | 0x0f;
345+
break;
346+
case 10:
347+
//右裸关节
348+
b[j] = 0xff << 24 | 0xff << 16 | 0xff << 8 | 0x0f;
349+
break;
350+
case 11:
351+
//左臀部
352+
b[j] = 0xff << 24 | 0x0f << 16 | 0xff << 8 | 0x0f;
353+
break;
354+
case 12:
355+
//左膝关节
356+
b[j] = 0xff << 24 | 0xff << 8 | 0x0f;
357+
break;
358+
case 13:
359+
//左裸关节
360+
b[j] = 0xff << 24 | 0x0f << 8 | 0x0f;
361+
break;
362+
case 14:
363+
//右眼
364+
b[j] = 0xff << 24 | 0x0f << 16 | 0x0f << 8 | 0x0f;
365+
break;
366+
case 15:
367+
//左眼
368+
b[j] = 0xff << 24 | 0x5f << 16;
369+
break;
370+
case 16:
371+
//右耳
372+
b[j] = 0xff << 24 | 0x5f << 8;
373+
break;
374+
case 17:
375+
//左耳
376+
b[j] = 0xff << 24 | 0xff << 16 | 0x0f;
377+
break;
378+
case 18:
379+
//背景（因为背景没有识别值 所以自动过滤掉了）
380+
b[j] = 0xff << 24 | 0x0f;
381+
break;
382+
case 19:
383+
//鼻子
384+
b[j] = 0xff << 24 | 0x0f << 8;
385+
break;
236386
}
237387
}
238388

‎image/body.gif

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