increase training crop + new edges for Karusell
increase training crop dtom 769 t o1281 for all models + 7 edges for Karusell dataset_v2_88
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@@ -4,7 +4,11 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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"id": "120b4212",
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"id": "120b4212",
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"metadata": {},
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"!python -m openpifpaf.predict test\\images\\test.png \\\n",
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"!python -m openpifpaf.predict test\\images\\test.png \\\n",
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@@ -18,7 +22,11 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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"id": "984d60d5",
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"id": "984d60d5",
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"metadata": {},
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"# !python -m openpifpaf.video --source test\\videos\\test.mp4 \\\n",
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"# !python -m openpifpaf.video --source test\\videos\\test.mp4 \\\n",
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@@ -51,4 +59,4 @@
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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"nbformat_minor": 5
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"nbformat_minor": 5
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}
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}
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@@ -2,9 +2,13 @@
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"cells": [
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"cells": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "9b13894c",
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"id": "9b13894c",
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"metadata": {},
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"import json\n",
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"import json\n",
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@@ -26,9 +30,13 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 178,
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"execution_count": 2,
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"id": "39604368",
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"id": "39604368",
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"metadata": {},
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"img = cv2.imread('Grand_Prix_GPS-double.png')\n",
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"img = cv2.imread('Grand_Prix_GPS-double.png')\n",
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@@ -41,7 +49,7 @@
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"df['z'] = 4.2*np.float32([0, 0, 0, 0, 3, 6.33, 7.33, 3.33, 2.33, 2.33, 2.33, 2.5, 2.5, 1.2,1.5])\n",
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"df['z'] = 4.2*np.float32([0, 0, 0, 0, 3, 6.33, 7.33, 3.33, 2.33, 2.33, 2.33, 2.5, 2.5, 1.2,1.5])\n",
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"df[['x', 'y']] /= 8.4\n",
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"df[['x', 'y']] /= 8.4\n",
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"df[['x', 'y']] = df.values[:, :2]@roration(-135) # поворот x и y в плоскости земли\n",
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"df[['x', 'y']] = df.values[:, :2]@roration(-135) # поворот x и y в плоскости земли\n",
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"# df.to_csv('GrandPrix_15kps.csv')\n",
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"df.to_csv('GrandPrix_15kps.csv')\n",
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"\n",
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"\n",
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"# rvec, tvec, camMatrx, dist = fit(img.shape[:2], df_raw, df)\n",
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"# rvec, tvec, camMatrx, dist = fit(img.shape[:2], df_raw, df)\n",
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"# imgpts = cv.projectPoints(5*arrow, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
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"# imgpts = cv.projectPoints(5*arrow, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
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@@ -54,7 +62,11 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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"id": "82d1f0f1",
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"id": "82d1f0f1",
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"metadata": {},
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"outputs": [],
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"source": []
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"source": []
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}
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}
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@@ -80,4 +92,4 @@
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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"nbformat_minor": 5
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"nbformat_minor": 5
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}
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}
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@@ -104,7 +104,14 @@ CAR_SKELETON_24 = [[ 1, 2],
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[58, 79],
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[58, 79],
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[88, 68],
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[88, 68],
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[69, 1],
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[69, 1],
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[37, 79]]
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[37, 79],
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[41, 32], #новые
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[54, 6],
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[51, 12],
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[48, 19],
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[44, 25],
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[84, 64],
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[64, 74]]
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CAR_CATEGORIES_24 = ['karusel']
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CAR_CATEGORIES_24 = ['karusel']
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