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HartornHartorn
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9 files changed

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-146
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‎examples/criteria1_partial_faces.ipynb‎

Lines changed: 15 additions & 22 deletions
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@@ -96,7 +96,7 @@
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"outputs": [],
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"source": [
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"chosen_idx = 4\n",
99-
"facial_part = FacialParts.upper_half\n",
99+
"facial_part = FacialParts.UPPER_HALF.value\n",
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"cropped_ds = CroppedDataLoader(ds, facial_part, crop_img=False)\n",
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"image, picked_landmarks, meta = cropped_ds[chosen_idx]"
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]
@@ -159,7 +159,7 @@
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" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
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" <p>Metric: <b>0.0623</b> (threshold = 1)</p>\n",
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" \n",
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" <p>Prediction time: 53.02 s.</p>\n",
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" <p>Prediction time: 20.22 s.</p>\n",
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" "
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],
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"text/plain": [
@@ -168,7 +168,7 @@
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" Description: Mean of normalised mean Euclidean distances across images\n",
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" Metric: 0.0623 (threshold = 1)\n",
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" \n",
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" Prediction time: 53.02 s.\n",
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" Prediction time: 20.22 s.\n",
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" "
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]
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},
@@ -385,20 +385,20 @@
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"data": {
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"text/html": [
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"\n",
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" <h4><span style=\"color:red;\">𐄂</span> Test \"NME_mean\" failed</h4>\n",
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" <h4><span style=\"color:green;\">✓</span> Test \"NME_mean\" succeeded</h4>\n",
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" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
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" <p>Metric: <b>1.3379</b> (threshold = 1)</p>\n",
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" <p>Metric: <b>0.5723</b> (threshold = 1)</p>\n",
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" \n",
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" <p>Prediction time: 1.07 s.</p>\n",
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" <p>Prediction time: 0.69 s.</p>\n",
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" "
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],
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"text/plain": [
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"\n",
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" Test \"NME_mean\" failed\n",
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" Test \"NME_mean\" succeeded\n",
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" Description: Mean of normalised mean Euclidean distances across images\n",
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" Metric: 1.3379 (threshold = 1)\n",
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" Metric: 0.5723 (threshold = 1)\n",
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" \n",
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" Prediction time: 1.07 s.\n",
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" Prediction time: 0.69 s.\n",
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" "
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]
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},
@@ -423,20 +423,20 @@
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"data": {
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"text/html": [
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"\n",
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" <h4><span style=\"color:red;\">𐄂</span> Test \"NME_std\" failed</h4>\n",
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" <h4><span style=\"color:green;\">✓</span> Test \"NME_std\" succeeded</h4>\n",
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" <p>Description: Standard deviation of normalised Mean Euclidean distances across images</p>\n",
428-
" <p>Metric: <b>2.6256</b> (threshold = 1)</p>\n",
428+
" <p>Metric: <b>0.7242</b> (threshold = 1)</p>\n",
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" \n",
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" <p>Prediction time: 1.06 s.</p>\n",
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" <p>Prediction time: 0.66 s.</p>\n",
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" "
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],
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"text/plain": [
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"\n",
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" Test \"NME_std\" failed\n",
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" Test \"NME_std\" succeeded\n",
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" Description: Standard deviation of normalised Mean Euclidean distances across images\n",
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" Metric: 2.6256 (threshold = 1)\n",
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" Metric: 0.7242 (threshold = 1)\n",
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" \n",
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" Prediction time: 1.06 s.\n",
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" Prediction time: 0.66 s.\n",
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" "
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]
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},
@@ -448,13 +448,6 @@
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"source": [
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"TestDiff(NMEStd, threshold=1).run(model, ds, CroppedDataLoader(ds, part=facial_part, margins=0.01))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎examples/ex1_draw_landmarks.ipynb‎

Lines changed: 3 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -191,7 +191,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
194-
"left eye\n",
194+
"FacialParts.LEFT_EYE\n",
195195
"NMEMean:0.03064837061887285\n",
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"NMEs:[0.03639644 0.02607136 0.02883627 0.02288638 0.0390514 ]\n",
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"MEMean:6.205209482416183\n",
@@ -262,10 +262,10 @@
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}
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],
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"source": [
265-
"prediction = model.predict(ds, facial_part=FacialParts.left_eye)\n",
265+
"prediction = model.predict(ds, facial_part=FacialParts.LEFT_EYE.value)\n",
266266
"marks = ds.all_marks\n",
267267
"\n",
268-
"print(FacialParts.left_eye.name)\n",
268+
"print(FacialParts.LEFT_EYE)\n",
269269
"for metric in [NMEMean, NMEs, MEMean, MEStd, NMEMean, NMEStd, Es]:\n",
270270
" print(f\"{metric.__name__}:{metric.get(prediction, marks)}\")"
271271
]
@@ -313,13 +313,6 @@
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"source": [
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"draw_marks(image, [ground_truth_landmarks, loreal_landmarks], [\"green\", \"red\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎examples/ex2_draw_partial_landmarks.ipynb‎

Lines changed: 25 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -81,7 +81,7 @@
8181
"from loreal_poc.marks.facial_parts import FacialParts\n",
8282
"\n",
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"chosen_idx = 4\n",
84-
"facial_parts = FacialParts.right_eye + FacialParts.right_eyebrow\n",
84+
"facial_parts = FacialParts.RIGHT_EYE.value + FacialParts.RIGHT_EYEBROW.value\n",
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"\n",
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"cropped_ds = CroppedDataLoader(ds, facial_parts, crop_img=False)\n",
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"\n",
@@ -114,6 +114,30 @@
114114
"draw_marks(image, [picked_landmarks], [\"green\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"FacialPart(name='(right eye + right eyebrow)', idx=array([False, False, False, False, False, False, False, False, False,\n",
127+
" False, False, False, False, False, False, False, False, False,\n",
128+
" False, False, False, False, True, True, True, True, True,\n",
129+
" False, False, False, False, False, False, False, False, False,\n",
130+
" False, False, False, False, False, False, True, True, True,\n",
131+
" True, True, True, False, False, False, False, False, False,\n",
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" False, False, False, False, False, False, False, False, False,\n",
133+
" False, False, False, False, False]))\n"
134+
]
135+
}
136+
],
137+
"source": [
138+
"print(facial_parts)"
139+
]
140+
},
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{
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"cell_type": "code",
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"execution_count": null,

‎examples/ex4_tests_and_metrics.ipynb‎

Lines changed: 11 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -100,7 +100,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {
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"collapsed": false,
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"jupyter": {
@@ -116,7 +116,7 @@
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" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
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" <p>Metric: <b>0.0623</b> (threshold = 1)</p>\n",
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" \n",
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" <p>Prediction time: 20.18 s.</p>\n",
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" <p>Prediction time: 20.02 s.</p>\n",
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" "
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],
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"text/plain": [
@@ -125,11 +125,11 @@
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" Description: Mean of normalised mean Euclidean distances across images\n",
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" Metric: 0.0623 (threshold = 1)\n",
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" \n",
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" Prediction time: 20.18 s.\n",
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" Prediction time: 20.02 s.\n",
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" "
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]
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},
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -140,7 +140,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
@@ -159,22 +159,22 @@
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"\n",
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" <h4><span style=\"color:green;\">✓</span> Test \"NME_mean\" succeeded</h4>\n",
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" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
162-
" <p>Metric: <b>0.6372</b> (threshold = 1)</p>\n",
162+
" <p>Metric: <b>0.3962</b> (threshold = 1)</p>\n",
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" Prediction fail rate: 0.4\n",
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" <p>Prediction time: 0.94 s.</p>\n",
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" <p>Prediction time: 0.69 s.</p>\n",
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" "
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],
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"text/plain": [
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"\n",
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" Test \"NME_mean\" succeeded\n",
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" Description: Mean of normalised mean Euclidean distances across images\n",
171-
" Metric: 0.6372 (threshold = 1)\n",
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" Metric: 0.3962 (threshold = 1)\n",
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" Prediction fail rate: 0.4\n",
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" Prediction time: 0.94 s.\n",
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" Prediction time: 0.69 s.\n",
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" "
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]
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},
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"execution_count": 8,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -183,7 +183,7 @@
183183
"from loreal_poc.marks.facial_parts import FacialParts\n",
184184
"from loreal_poc.dataloaders.wrappers import CroppedDataLoader, CachedDataLoader\n",
185185
"\n",
186-
"facial_part = FacialParts.left_half\n",
186+
"facial_part = FacialParts.LEFT_HALF.value\n",
187187
"\n",
188188
"TestDiff(metric=NMEMean, threshold=1).run(model, ds, CroppedDataLoader(ds, part=facial_part), facial_part=facial_part)"
189189
]

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