"Label"	"PyLabel"	"Mean"	"Length"	"Method"	"Pigmentation"
"1"	NA	"1"	89.771	9.577	"ImageJ"	0.647956862745098
"2"	NA	"2"	78.403	12.592	"ImageJ"	0.692537254901961
"3"	NA	"3"	63.338	9.449	"ImageJ"	0.75161568627451
"4"	NA	"4"	105.548	12.726	"ImageJ"	0.586086274509804
"5"	NA	"5"	98.583	8.796	"ImageJ"	0.6134
"6"	NA	"6"	83.508	10.849	"ImageJ"	0.672517647058824
"7"	NA	"7"	90.564	8.912	"ImageJ"	0.644847058823529
"8"	NA	"8"	37.991	8.842	"ImageJ"	0.85101568627451
"9"	NA	"9"	93.67	9.617	"ImageJ"	0.632666666666667
"10"	NA	"10"	100.835	7.38	"ImageJ"	0.604568627450981
"12"	NA	"12"	91.641	8.867	"ImageJ"	0.640623529411765
"13"	NA	"13"	86.721	9.202	"ImageJ"	0.659917647058823
"14"	NA	"14"	78.012	9.176	"ImageJ"	0.694070588235294
"15"	NA	"15"	35.096	10.944	"ImageJ"	0.86236862745098
"16"	NA	"16"	38.65	8.891	"ImageJ"	0.84843137254902
"17"	NA	"17"	66.979	10.3	"ImageJ"	0.737337254901961
"18"	NA	"18"	78.943	9.085	"ImageJ"	0.690419607843137
"19"	NA	"19"	66.462	7.12	"ImageJ"	0.739364705882353
"20"	NA	"20"	78.794	8.29	"ImageJ"	0.691003921568628
"21"	NA	"1"	88.53	10.694	"ImageJ"	0.652823529411765
"22"	NA	"2"	80.475	13.06	"ImageJ"	0.684411764705882
"23"	NA	"3"	65.858	9.109	"ImageJ"	0.741733333333333
"24"	NA	"4"	107.482	12.587	"ImageJ"	0.578501960784314
"25"	NA	"5"	101.51	9.013	"ImageJ"	0.601921568627451
"26"	NA	"6"	83.452	10.61	"ImageJ"	0.672737254901961
"27"	NA	"7"	89.972	7.968	"ImageJ"	0.64716862745098
"28"	NA	"8"	36.833	9.539	"ImageJ"	0.855556862745098
"29"	NA	"9"	95.32	9.151	"ImageJ"	0.626196078431373
"30"	NA	"10"	105.26	6.471	"ImageJ"	0.58721568627451
"32"	NA	"12"	94.756	9.169	"ImageJ"	0.628407843137255
"33"	NA	"13"	90.859	9.212	"ImageJ"	0.643690196078431
"34"	NA	"14"	79.749	9.189	"ImageJ"	0.687258823529412
"35"	NA	"15"	32.721	10.558	"ImageJ"	0.871682352941176
"36"	NA	"16"	32.834	8.237	"ImageJ"	0.871239215686274
"37"	NA	"17"	67.262	10.506	"ImageJ"	0.736227450980392
"38"	NA	"18"	78.984	8.952	"ImageJ"	0.690258823529412
"39"	NA	"19"	77.012	8.487	"ImageJ"	0.697992156862745
"40"	NA	"20"	80.765	8.389	"ImageJ"	0.683274509803922
"41"	NA	"1"	89.307	8.691	"ImageJ"	0.649776470588235
"42"	NA	"2"	78.284	12.216	"ImageJ"	0.693003921568627
"43"	NA	"3"	62.649	9.099	"ImageJ"	0.754317647058824
"44"	NA	"4"	106.678	12.819	"ImageJ"	0.581654901960784
"45"	NA	"5"	100.968	8.764	"ImageJ"	0.604047058823529
"46"	NA	"6"	84.49	10.869	"ImageJ"	0.668666666666667
"47"	NA	"7"	91.441	8.901	"ImageJ"	0.641407843137255
"48"	NA	"8"	37.832	9.185	"ImageJ"	0.851639215686275
"49"	NA	"9"	92.859	9.287	"ImageJ"	0.63584705882353
"50"	NA	"10"	102.424	7.186	"ImageJ"	0.598337254901961
"52"	NA	"12"	93.427	8.992	"ImageJ"	0.633619607843137
"53"	NA	"13"	89.121	9.561	"ImageJ"	0.650505882352941
"54"	NA	"14"	77.781	9.183	"ImageJ"	0.694976470588235
"55"	NA	"15"	31.537	10.591	"ImageJ"	0.876325490196078
"56"	NA	"16"	35.745	8.473	"ImageJ"	0.859823529411765
"57"	NA	"17"	67.242	10.31	"ImageJ"	0.736305882352941
"58"	NA	"18"	79.242	9.206	"ImageJ"	0.689247058823529
"59"	NA	"19"	67.948	7.266	"ImageJ"	0.733537254901961
"60"	NA	"20"	80.341	8.28	"ImageJ"	0.684937254901961
"61"	NA	"1"	90.588	8.87	"ImageJ"	0.644752941176471
"62"	NA	"2"	79.258	13.103	"ImageJ"	0.68918431372549
"63"	NA	"3"	64.18	9.096	"ImageJ"	0.748313725490196
"64"	NA	"4"	108.228	12.817	"ImageJ"	0.575576470588235
"65"	NA	"5"	102.64	8.765	"ImageJ"	0.597490196078431
"66"	NA	"6"	85.365	10.745	"ImageJ"	0.665235294117647
"67"	NA	"7"	93.723	8.965	"ImageJ"	0.632458823529412
"68"	NA	"8"	39.009	9.169	"ImageJ"	0.847023529411765
"69"	NA	"9"	94.875	10.085	"ImageJ"	0.627941176470588
"70"	NA	"10"	107.104	6.556	"ImageJ"	0.57998431372549
"72"	NA	"12"	94.272	8.927	"ImageJ"	0.630305882352941
"73"	NA	"13"	89.302	9.462	"ImageJ"	0.649796078431372
"74"	NA	"14"	79.659	9.097	"ImageJ"	0.687611764705882
"75"	NA	"15"	32.232	10.711	"ImageJ"	0.8736
"76"	NA	"16"	37.282	8.777	"ImageJ"	0.853796078431373
"77"	NA	"17"	67.667	10.256	"ImageJ"	0.734639215686275
"78"	NA	"18"	81.792	9.019	"ImageJ"	0.679247058823529
"79"	NA	"19"	69.146	7.143	"ImageJ"	0.728839215686274
"80"	NA	"20"	83.465	8.341	"ImageJ"	0.672686274509804
"81"	NA	"1"	91.346	8.82	"ImageJ"	0.641780392156863
"82"	NA	"2"	80.208	13.174	"ImageJ"	0.685458823529412
"83"	NA	"3"	62.738	9.497	"ImageJ"	0.75396862745098
"84"	NA	"4"	108.632	12.806	"ImageJ"	0.573992156862745
"85"	NA	"5"	102.74	8.723	"ImageJ"	0.597098039215686
"86"	NA	"6"	86.974	10.837	"ImageJ"	0.658925490196078
"87"	NA	"7"	94.245	8.879	"ImageJ"	0.630411764705882
"88"	NA	"8"	39.039	9.287	"ImageJ"	0.846905882352941
"89"	NA	"9"	96.627	10.62	"ImageJ"	0.621070588235294
"90"	NA	"10"	106.538	7.402	"ImageJ"	0.582203921568627
"92"	NA	"12"	95.054	9.252	"ImageJ"	0.627239215686274
"93"	NA	"13"	89.406	9.284	"ImageJ"	0.649388235294118
"94"	NA	"14"	82.044	9.169	"ImageJ"	0.678258823529412
"95"	NA	"15"	31.796	10.952	"ImageJ"	0.875309803921569
"96"	NA	"16"	32.338	8.718	"ImageJ"	0.87318431372549
"97"	NA	"17"	66.076	10.182	"ImageJ"	0.740878431372549
"98"	NA	"18"	79.109	9.211	"ImageJ"	0.68976862745098
"99"	NA	"19"	67.571	7.205	"ImageJ"	0.73501568627451
"100"	NA	"20"	82.772	8.219	"ImageJ"	0.675403921568627
"101"	NA	"1"	89.96	8.575	"ImageJ"	0.64721568627451
"102"	NA	"2"	79.115	12.847	"ImageJ"	0.689745098039216
"103"	NA	"3"	63.755	9.292	"ImageJ"	0.749980392156863
"104"	NA	"4"	109.44	12.737	"ImageJ"	0.570823529411765
"105"	NA	"5"	102.494	8.986	"ImageJ"	0.598062745098039
"106"	NA	"6"	85.05	10.777	"ImageJ"	0.666470588235294
"107"	NA	"7"	91.746	8.669	"ImageJ"	0.640211764705882
"108"	NA	"8"	36.06	8.914	"ImageJ"	0.858588235294118
"109"	NA	"9"	95.565	9.808	"ImageJ"	0.625235294117647
"110"	NA	"10"	105.517	6.515	"ImageJ"	0.586207843137255
"112"	NA	"12"	93.28	9.512	"ImageJ"	0.634196078431372
"113"	NA	"13"	89.697	9.44	"ImageJ"	0.648247058823529
"114"	NA	"14"	81.292	9.165	"ImageJ"	0.681207843137255
"115"	NA	"15"	32.971	12.342	"ImageJ"	0.870701960784314
"116"	NA	"16"	37.391	8.783	"ImageJ"	0.85336862745098
"117"	NA	"17"	64.729	10.128	"ImageJ"	0.746160784313726
"118"	NA	"18"	79.947	9.085	"ImageJ"	0.686482352941176
"119"	NA	"19"	68.284	7.091	"ImageJ"	0.732219607843137
"120"	NA	"20"	79.713	8.08	"ImageJ"	0.6874
"121"	NA	"1"	90.094	8.733	"ImageJ"	0.646690196078431
"122"	NA	"2"	79.657	12.721	"ImageJ"	0.687619607843137
"123"	NA	"3"	64.808	9.36	"ImageJ"	0.745850980392157
"124"	NA	"4"	106.642	12.602	"ImageJ"	0.581796078431373
"125"	NA	"5"	103.104	9.304	"ImageJ"	0.595670588235294
"126"	NA	"6"	87.058	10.995	"ImageJ"	0.658596078431373
"127"	NA	"7"	91.506	8.204	"ImageJ"	0.641152941176471
"128"	NA	"8"	36.865	9.377	"ImageJ"	0.85543137254902
"129"	NA	"9"	94.912	9.891	"ImageJ"	0.627796078431373
"130"	NA	"10"	104.543	6.542	"ImageJ"	0.590027450980392
"132"	NA	"12"	93.451	9.524	"ImageJ"	0.633525490196079
"133"	NA	"13"	88.85	9.573	"ImageJ"	0.65156862745098
"134"	NA	"14"	80.01	9.132	"ImageJ"	0.686235294117647
"135"	NA	"15"	31.069	10.97	"ImageJ"	0.878160784313726
"136"	NA	"16"	34.892	8.56	"ImageJ"	0.86316862745098
"137"	NA	"17"	65.366	10.157	"ImageJ"	0.743662745098039
"138"	NA	"18"	78.319	8.923	"ImageJ"	0.692866666666667
"139"	NA	"19"	68.508	6.948	"ImageJ"	0.731341176470588
"140"	NA	"20"	82.095	8.269	"ImageJ"	0.678058823529412
"141"	NA	"1"	91.955	8.88	"ImageJ"	0.639392156862745
"142"	NA	"2"	81.746	13.608	"ImageJ"	0.679427450980392
"143"	NA	"3"	65.469	9.524	"ImageJ"	0.743258823529412
"144"	NA	"4"	109.028	12.63	"ImageJ"	0.572439215686274
"145"	NA	"5"	105.52	8.9	"ImageJ"	0.586196078431373
"146"	NA	"6"	88.089	10.923	"ImageJ"	0.654552941176471
"147"	NA	"7"	93.105	8.399	"ImageJ"	0.634882352941176
"148"	NA	"8"	37.234	9.388	"ImageJ"	0.85398431372549
"149"	NA	"9"	94.856	9.586	"ImageJ"	0.62801568627451
"150"	NA	"10"	107.519	6.54	"ImageJ"	0.578356862745098
"152"	NA	"12"	94.327	8.946	"ImageJ"	0.630090196078431
"153"	NA	"13"	89.505	9.07	"ImageJ"	0.649
"154"	NA	"14"	81.998	9.376	"ImageJ"	0.678439215686275
"155"	NA	"15"	32.493	11.118	"ImageJ"	0.872576470588235
"156"	NA	"16"	33.851	9.088	"ImageJ"	0.867250980392157
"157"	NA	"17"	66.062	10.058	"ImageJ"	0.740933333333333
"158"	NA	"18"	81.216	9.049	"ImageJ"	0.681505882352941
"159"	NA	"19"	71.428	7.035	"ImageJ"	0.719890196078431
"160"	NA	"20"	83.923	8.356	"ImageJ"	0.670890196078431
"161"	NA	"1"	91.741	8.784	"ImageJ"	0.64023137254902
"162"	NA	"2"	80.633	12.997	"ImageJ"	0.683792156862745
"163"	NA	"3"	65.788	9.578	"ImageJ"	0.742007843137255
"164"	NA	"4"	109.103	12.799	"ImageJ"	0.572145098039216
"165"	NA	"5"	104.785	8.889	"ImageJ"	0.589078431372549
"166"	NA	"6"	87.707	11.363	"ImageJ"	0.656050980392157
"167"	NA	"7"	91.206	7.392	"ImageJ"	0.642329411764706
"168"	NA	"8"	38.951	9.697	"ImageJ"	0.847250980392157
"169"	NA	"9"	96.21	10.376	"ImageJ"	0.622705882352941
"170"	NA	"10"	107.606	6.484	"ImageJ"	0.57801568627451
"172"	NA	"12"	94.779	9.037	"ImageJ"	0.628317647058824
"173"	NA	"13"	90.692	9.367	"ImageJ"	0.644345098039216
"174"	NA	"14"	79.939	9.278	"ImageJ"	0.686513725490196
"175"	NA	"15"	34.122	12.82	"ImageJ"	0.866188235294118
"176"	NA	"16"	34.2	8.602	"ImageJ"	0.865882352941177
"177"	NA	"17"	65.423	10.164	"ImageJ"	0.743439215686274
"178"	NA	"18"	81.941	11.015	"ImageJ"	0.678662745098039
"179"	NA	"19"	71.156	7.295	"ImageJ"	0.720956862745098
"180"	NA	"20"	81.484	8.072	"ImageJ"	0.680454901960784
"181"	NA	"1"	90.845	8.857	"ImageJ"	0.643745098039216
"182"	NA	"2"	81.756	12.583	"ImageJ"	0.679388235294118
"183"	NA	"3"	66.285	9.413	"ImageJ"	0.740058823529412
"184"	NA	"4"	110.614	13.096	"ImageJ"	0.566219607843137
"185"	NA	"5"	104.282	8.679	"ImageJ"	0.591050980392157
"186"	NA	"6"	91.306	11.175	"ImageJ"	0.641937254901961
"187"	NA	"7"	92.198	8.199	"ImageJ"	0.638439215686275
"188"	NA	"8"	41.115	9.401	"ImageJ"	0.838764705882353
"189"	NA	"9"	96.408	8.972	"ImageJ"	0.621929411764706
"190"	NA	"10"	110.079	7.083	"ImageJ"	0.568317647058823
"192"	NA	"12"	96.252	8.823	"ImageJ"	0.622541176470588
"193"	NA	"13"	91.775	9.624	"ImageJ"	0.640098039215686
"194"	NA	"14"	82.629	9.29	"ImageJ"	0.675964705882353
"195"	NA	"15"	36.089	12.934	"ImageJ"	0.858474509803922
"196"	NA	"16"	34.342	8.619	"ImageJ"	0.865325490196079
"197"	NA	"17"	64.488	10.282	"ImageJ"	0.747105882352941
"198"	NA	"18"	82.209	9.123	"ImageJ"	0.677611764705882
"199"	NA	"19"	70.244	7.205	"ImageJ"	0.724533333333333
"200"	NA	"20"	80.418	8.28	"ImageJ"	0.684635294117647
"201"	"1"	"1"	98.9	8.87	"Python"	0.612156862745098
"202"	"1"	"2"	87.26	12.69	"Python"	0.657803921568627
"203"	"1"	"3"	70.94	9.12	"Python"	0.721803921568627
"204"	"1"	"4"	118.65	12.76	"Python"	0.534705882352941
"205"	"1"	"5"	107.17	8.51	"Python"	0.579725490196078
"206"	"1"	"6"	93.43	10.88	"Python"	0.633607843137255
"207"	"1"	"7"	98.27	8.62	"Python"	0.614627450980392
"208"	"1"	"8"	39.56	9.53	"Python"	0.844862745098039
"209"	"1"	"9"	103.94	9.42	"Python"	0.592392156862745
"210"	"1"	"10"	115.04	6.59	"Python"	0.548862745098039
"212"	"1"	"12"	102.17	9.02	"Python"	0.599333333333333
"213"	"1"	"13"	95.5	9.31	"Python"	0.625490196078431
"214"	"1"	"14"	86.14	9.02	"Python"	0.662196078431373
"215"	"1"	"15"	46.91	11.6	"Python"	0.816039215686275
"216"	"1"	"16"	40.68	8.64	"Python"	0.840470588235294
"217"	"1"	"17"	87.55	11.85	"Python"	0.656666666666667
"218"	"1"	"18"	84.44	9.1	"Python"	0.668862745098039
"219"	"1"	"19"	72.3	7.12	"Python"	0.716470588235294
"220"	"1"	"20"	101.7	8.66	"Python"	0.601176470588235
"221"	"2"	"1"	98.38	8.89	"Python"	0.614196078431373
"222"	"2"	"2"	90.2	13.22	"Python"	0.646274509803922
"223"	"2"	"3"	71.08	8.83	"Python"	0.721254901960784
"224"	"2"	"4"	122.78	12.31	"Python"	0.518509803921569
"225"	"2"	"5"	110.42	8.43	"Python"	0.566980392156863
"226"	"2"	"6"	94.96	10.84	"Python"	0.627607843137255
"227"	"2"	"7"	96.29	7.96	"Python"	0.622392156862745
"228"	"2"	"8"	43.83	9.53	"Python"	0.828117647058824
"229"	"2"	"9"	106.61	8.79	"Python"	0.581921568627451
"230"	"2"	"10"	118.39	6.32	"Python"	0.535725490196079
"232"	"2"	"12"	106.08	9.46	"Python"	0.584
"233"	"2"	"13"	99.56	9.53	"Python"	0.60956862745098
"234"	"2"	"14"	88.69	8.96	"Python"	0.652196078431373
"235"	"2"	"15"	37.46	11.74	"Python"	0.853098039215686
"236"	"2"	"16"	33.79	8.45	"Python"	0.867490196078431
"237"	"2"	"17"	93.03	11.43	"Python"	0.635176470588235
"238"	"2"	"18"	84.9	8.58	"Python"	0.667058823529412
"239"	"2"	"19"	76.9	7.24	"Python"	0.69843137254902
"240"	"2"	"20"	91.33	8.87	"Python"	0.641843137254902
"241"	"3"	"1"	99.13	9.06	"Python"	0.611254901960784
"242"	"3"	"2"	87.5	12.67	"Python"	0.656862745098039
"243"	"3"	"3"	70.8	9.12	"Python"	0.722352941176471
"244"	"3"	"4"	121.18	12.97	"Python"	0.52478431372549
"245"	"3"	"5"	110.49	8.55	"Python"	0.566705882352941
"246"	"3"	"6"	94.01	10.86	"Python"	0.631333333333333
"247"	"3"	"7"	98.41	8.55	"Python"	0.614078431372549
"248"	"3"	"8"	37.6	9.23	"Python"	0.852549019607843
"249"	"3"	"9"	102.6	9.29	"Python"	0.597647058823529
"250"	"3"	"10"	117.49	6.55	"Python"	0.539254901960784
"252"	"3"	"12"	105.02	9.15	"Python"	0.588156862745098
"253"	"3"	"13"	98.24	9.29	"Python"	0.614745098039216
"254"	"3"	"14"	86.93	9.06	"Python"	0.659098039215686
"255"	"3"	"15"	48.35	11.57	"Python"	0.810392156862745
"256"	"3"	"16"	36.73	8.64	"Python"	0.855960784313726
"257"	"3"	"17"	86.66	12.44	"Python"	0.660156862745098
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