Examples of using the Image analysis BLOCK
Examples of using the Image analysis BLOCK
This document is a collection of several examples of image analysis from using the Image analysis BLOCK . The examples of each detection type listed below contain both the image used and the analysis results.
- Facial recognition example
- Landmark recognition example
- Logo recognition example
- Object recognition example
- OCR (text) example
- Adult content detection example
- Color analysis example
The results are shown as displayed in the Logs section by using an Output to log BLOCK.
check_box For a detailed example of using the Image analysis BLOCK, refer to Using the Google Cloud Vision API Machine Learning service .
Facial recognition example
Below are an image and the results from using facial recognition to analyze it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > Facial recognition ("faceAnnotations") .
{
"faceAnnotations": [
{
"tiltAngle": 4.6994467,
"underExposedLikelihood": "VERY_UNLIKELY",
"fdBoundingPoly": {
"vertices": [
{
"y": 318,
"x": 670
},
{
"y": 318,
"x": 959
},
{
"y": 606,
"x": 959
},
{
"y": 606,
"x": 670
}
]
},
"landmarkingConfidence": 0.8396889,
"joyLikelihood": "VERY_UNLIKELY",
"detectionConfidence": 0.95103228,
"surpriseLikelihood": "VERY_UNLIKELY",
"angerLikelihood": "VERY_UNLIKELY",
"headwearLikelihood": "VERY_UNLIKELY",
"panAngle": 1.1417893,
"boundingPoly": {
"vertices": [
{
"y": 203,
"x": 605
},
{
"y": 203,
"x": 1017
},
{
"y": 682,
"x": 1017
},
{
"y": 682,
"x": 605
}
]
},
"landmarks": [
{
"position": {
"y": 406.18475,
"x": 755.82172,
"z": -0.0019571674
},
"type": "LEFT_EYE"
},
{
"position": {
"y": 411.55664,
"x": 873.33008,
"z": 2.2569208
},
"type": "RIGHT_EYE"
},
{
"position": {
"y": 378.80887,
"x": 712.91016,
"z": 12.889206
},
"type": "LEFT_OF_LEFT_EYEBROW"
},
{
"position": {
"y": 377.71161,
"x": 787.5188,
"z": -21.346836
},
"type": "RIGHT_OF_LEFT_EYEBROW"
},
{
"position": {
"y": 380.31638,
"x": 847.68329,
"z": -20.1784
},
"type": "LEFT_OF_RIGHT_EYEBROW"
},
{
"position": {
"y": 386.27527,
"x": 920.77515,
"z": 16.990841
},
"type": "RIGHT_OF_RIGHT_EYEBROW"
},
{
"position": {
"y": 405.4343,
"x": 815.85791,
"z": -24.665674
},
"type": "MIDPOINT_BETWEEN_EYES"
},
{
"position": {
"y": 474.37393,
"x": 818.08716,
"z": -67.114952
},
"type": "NOSE_TIP"
},
{
"position": {
"y": 527.74731,
"x": 813.43134,
"z": -41.542439
},
"type": "UPPER_LIP"
},
{
"position": {
"y": 558.07959,
"x": 810.74756,
"z": -36.031929
},
"type": "LOWER_LIP"
},
{
"position": {
"y": 538.40521,
"x": 758.57562,
"z": -9.306365
},
"type": "MOUTH_LEFT"
},
{
"position": {
"y": 545.70831,
"x": 864.68909,
"z": -7.6972365
},
"type": "MOUTH_RIGHT"
},
{
"position": {
"y": 541.5047,
"x": 812.28436,
"z": -33.855068
},
"type": "MOUTH_CENTER"
},
{
"position": {
"y": 490.774,
"x": 848.905,
"z": -19.081556
},
"type": "NOSE_BOTTOM_RIGHT"
},
{
"position": {
"y": 486.44943,
"x": 781.81824,
"z": -20.955832
},
"type": "NOSE_BOTTOM_LEFT"
},
{
"position": {
"y": 494.91971,
"x": 815.83813,
"z": -39.895447
},
"type": "NOSE_BOTTOM_CENTER"
},
{
"position": {
"y": 399.35587,
"x": 754.37134,
"z": -7.7445889
},
"type": "LEFT_EYE_TOP_BOUNDARY"
},
{
"position": {
"y": 410.89209,
"x": 777.84955,
"z": 0.76056117
},
"type": "LEFT_EYE_RIGHT_CORNER"
},
{
"position": {
"y": 414.91089,
"x": 754.63379,
"z": -1.8167982
},
"type": "LEFT_EYE_BOTTOM_BOUNDARY"
},
{
"position": {
"y": 408.99341,
"x": 729.02216,
"z": 10.692088
},
"type": "LEFT_EYE_LEFT_CORNER"
},
{
"position": {
"y": 408.29578,
"x": 751.95062,
"z": -3.1911113
},
"type": "LEFT_EYE_PUPIL"
},
{
"position": {
"y": 403.79346,
"x": 878.7066,
"z": -5.291678
},
"type": "RIGHT_EYE_TOP_BOUNDARY"
},
{
"position": {
"y": 415.23694,
"x": 902.60089,
"z": 14.143926
},
"type": "RIGHT_EYE_RIGHT_CORNER"
},
{
"position": {
"y": 419.31335,
"x": 874.99078,
"z": 0.52098614
},
"type": "RIGHT_EYE_BOTTOM_BOUNDARY"
},
{
"position": {
"y": 414.07681,
"x": 851.71515,
"z": 2.097218
},
"type": "RIGHT_EYE_LEFT_CORNER"
},
{
"position": {
"y": 412.89627,
"x": 879.66083,
"z": -0.84396207
},
"type": "RIGHT_EYE_PUPIL"
},
{
"position": {
"y": 362.69925,
"x": 750.49707,
"z": -11.22148
},
"type": "LEFT_EYEBROW_UPPER_MIDPOINT"
},
{
"position": {
"y": 367.53323,
"x": 885.549,
"z": -8.5894537
},
"type": "RIGHT_EYEBROW_UPPER_MIDPOINT"
},
{
"position": {
"y": 479.10718,
"x": 669.34882,
"z": 146.48503
},
"type": "LEFT_EAR_TRAGION"
},
{
"position": {
"y": 489.83243,
"x": 951.98108,
"z": 152.26144
},
"type": "RIGHT_EAR_TRAGION"
},
{
"position": {
"y": 377.32367,
"x": 817.88141,
"z": -25.309021
},
"type": "FOREHEAD_GLABELLA"
},
{
"position": {
"y": 621.589,
"x": 809.107,
"z": -22.926865
},
"type": "CHIN_GNATHION"
},
{
"position": {
"y": 555.0899,
"x": 681.2428,
"z": 89.758865
},
"type": "CHIN_LEFT_GONION"
},
{
"position": {
"y": 564.30884,
"x": 936.77637,
"z": 94.8158
},
"type": "CHIN_RIGHT_GONION"
}
],
"blurredLikelihood": "VERY_UNLIKELY",
"rollAngle": 1.9417542,
"sorrowLikelihood": "VERY_UNLIKELY"
}
],
"gcs_url": "gs://vision-api-samples/face_detection_sample.jpg",
"timestamp": 1475731991.0
}
Landmark recognition example
Below are an image and the results from using landmark recognition to analyze it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > Landmark recognition ("landmarkAnnotations") .
{
"landmarkAnnotations": [
{
"mid": "/m/072p8",
"description": "Statue of Liberty",
"score": 0.87311679,
"boundingPoly": {
"vertices": [
{
"y": 63,
"x": 240
},
{
"y": 63,
"x": 365
},
{
"y": 467,
"x": 365
},
{
"y": 467,
"x": 240
}
]
},
"locations": [
{
"latLng": {
"latitude": 40.689261,
"longitude": -74.044482
}
}
]
}
],
"gcs_url": "gs://vision-api-samples/landmark_detection_sample.jpg",
"timestamp": 1475664897.0
}
Logo recognition example
Below are an image and the results from using logo recognition to analyze it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > Logo recognition ("logoAnnotations") .
{
"logoAnnotations": [
{
"mid": "/m/05nrd2",
"description": "Coca-Cola Zero",
"score": 0.19073276,
"boundingPoly": {
"vertices": [
{
"y": 69,
"x": 337
},
{
"y": 69,
"x": 395
},
{
"y": 119,
"x": 395
},
{
"y": 119,
"x": 337
}
]
}
}
],
"gcs_url": "gs://vision-api-samples/logo_detection_sample.jpg",
"timestamp": 1475730505.0
}
Object recognition sample
Below are an image and the results from using object recognition to analyze it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > Object recognition ("labelAnnotations") .
{
"labelAnnotations": [
{
"mid": "/m/0kpqd",
"description": "watermelon",
"score": 0.92826855
},
{
"mid": "/m/016rbg",
"description": "melon",
"score": 0.92359751
},
{
"mid": "/m/02wbm",
"description": "food",
"score": 0.91787922
},
{
"mid": "/m/01f5gx",
"description": "eating",
"score": 0.8840431
},
{
"mid": "/m/02xwb",
"description": "fruit",
"score": 0.88381821
}
],
"gcs_url": "gs://vision-api-samples/label_detection_sample.jpg",
"timestamp": 1475723844.0
}
OCR ("textAnnotations") example
Below are an image and the results from using OCR (text) to analyze it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > OCR ("textAnnotations") .
{
"textAnnotations": [
{
"description": "ATTENZIONE\nParcheggio non custodito\norario apertura:\n07.30 20.00\nCHIUSURA AUTOMATICA\nDEI CANCELLI\nALLE ORE 20.00\n",
"locale": "it",
"boundingPoly": {
"vertices": [
{
"y": 257,
"x": 115
},
{
"y": 257,
"x": 417
},
{
"y": 536,
"x": 417
},
{
"y": 536,
"x": 115
}
]
}
},
{
"description": "ATTENZIONE",
"boundingPoly": {
"vertices": [
{
"y": 257,
"x": 139
},
{
"y": 257,
"x": 394
},
{
"y": 289,
"x": 394
},
{
"y": 289,
"x": 139
}
]
}
},
{
"description": "Parcheggio",
"boundingPoly": {
"vertices": [
{
"y": 314,
"x": 115
},
{
"y": 314,
"x": 243
},
{
"y": 339,
"x": 243
},
{
"y": 339,
"x": 115
}
]
}
},
{
"description": "non",
"boundingPoly": {
"vertices": [
{
"y": 314,
"x": 255
},
{
"y": 314,
"x": 295
},
{
"y": 339,
"x": 295
},
{
"y": 339,
"x": 255
}
]
}
},
{
"description": "custodito",
"boundingPoly": {
"vertices": [
{
"y": 314,
"x": 307
},
{
"y": 314,
"x": 417
},
{
"y": 339,
"x": 417
},
{
"y": 339,
"x": 307
}
]
}
},
{
"description": "orario",
"boundingPoly": {
"vertices": [
{
"y": 348,
"x": 174
},
{
"y": 348,
"x": 243
},
{
"y": 375,
"x": 243
},
{
"y": 375,
"x": 174
}
]
}
},
{
"description": "apertura:",
"boundingPoly": {
"vertices": [
{
"y": 349,
"x": 252
},
{
"y": 349,
"x": 359
},
{
"y": 374,
"x": 359
},
{
"y": 374,
"x": 252
}
]
}
},
{
"description": "07.30",
"boundingPoly": {
"vertices": [
{
"y": 382,
"x": 183
},
{
"y": 382,
"x": 251
},
{
"y": 405,
"x": 251
},
{
"y": 405,
"x": 183
}
]
}
},
{
"description": "20.00",
"boundingPoly": {
"vertices": [
{
"y": 382,
"x": 277
},
{
"y": 382,
"x": 350
},
{
"y": 405,
"x": 350
},
{
"y": 405,
"x": 277
}
]
}
},
{
"description": "CHIUSURA",
"boundingPoly": {
"vertices": [
{
"y": 435,
"x": 140
},
{
"y": 435,
"x": 253
},
{
"y": 459,
"x": 253
},
{
"y": 459,
"x": 140
}
]
}
},
{
"description": "AUTOMATICA",
"boundingPoly": {
"vertices": [
{
"y": 435,
"x": 266
},
{
"y": 435,
"x": 393
},
{
"y": 459,
"x": 393
},
{
"y": 459,
"x": 266
}
]
}
},
{
"description": "DEI",
"boundingPoly": {
"vertices": [
{
"y": 472,
"x": 196
},
{
"y": 472,
"x": 231
},
{
"y": 500,
"x": 231
},
{
"y": 500,
"x": 196
}
]
}
},
{
"description": "CANCELLI",
"boundingPoly": {
"vertices": [
{
"y": 471,
"x": 241
},
{
"y": 470,
"x": 338
},
{
"y": 498,
"x": 338
},
{
"y": 499,
"x": 241
}
]
}
},
{
"description": "ALLE",
"boundingPoly": {
"vertices": [
{
"y": 510,
"x": 181
},
{
"y": 510,
"x": 228
},
{
"y": 536,
"x": 228
},
{
"y": 536,
"x": 181
}
]
}
},
{
"description": "ORE",
"boundingPoly": {
"vertices": [
{
"y": 510,
"x": 240
},
{
"y": 510,
"x": 278
},
{
"y": 536,
"x": 278
},
{
"y": 536,
"x": 240
}
]
}
},
{
"description": "20.00",
"boundingPoly": {
"vertices": [
{
"y": 510,
"x": 290
},
{
"y": 510,
"x": 354
},
{
"y": 536,
"x": 354
},
{
"y": 536,
"x": 290
}
]
}
}
],
"gcs_url": "gs://vision-api-samples/text_detection_sample.jpg",
"timestamp": 1475662827.0
}
Adult content detection example
Below are an image and the results from using adult content detection to analyze it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > Adult content detection ("safeSearchAnnotation") .
{
"safeSearchAnnotation": {
"medical": "VERY_UNLIKELY",
"spoof": "VERY_UNLIKELY",
"violence": "UNLIKELY",
"adult": "VERY_UNLIKELY"
},
"gcs_url": "gs://vision-api-samples/safe_search_detection_sample.jpg",
"timestamp": 1475719652.0
}
Color analysis example
Below are an image and the results from using color analysis on it. For details on data format, refer to BLOCKS Reference > Output specifications > Image analysis > Color analysis ("imagePropertiesAnnotation") .
{
"imagePropertiesAnnotation": {
"dominantColors": {
"colors": [
{
"pixelFraction": 0.03975622,
"color": {
"green": 23,
"blue": 24,
"red": 19
},
"score": 0.15343559
},
{
"pixelFraction": 0.25660831,
"color": {
"green": 213,
"blue": 130,
"red": 156
},
"score": 0.089890622
},
{
"pixelFraction": 0.0071575367,
"color": {
"green": 243,
"blue": 245,
"red": 234
},
"score": 0.044020534
},
{
"pixelFraction": 0.0072284034,
"color": {
"green": 165,
"blue": 124,
"red": 131
},
"score": 0.030882463
},
{
"pixelFraction": 0.02898448,
"color": {
"green": 52,
"blue": 54,
"red": 46
},
"score": 0.10213716
},
{
"pixelFraction": 0.027141945,
"color": {
"green": 85,
"blue": 89,
"red": 76
},
"score": 0.085689932
},
{
"pixelFraction": 0.018425342,
"color": {
"green": 119,
"blue": 122,
"red": 109
},
"score": 0.053121205
},
{
"pixelFraction": 0.17454468,
"color": {
"green": 209,
"blue": 101,
"red": 144
},
"score": 0.036085345
},
{
"pixelFraction": 0.0090000713,
"color": {
"green": 116,
"blue": 136,
"red": 107
},
"score": 0.033624593
},
{
"pixelFraction": 0.028913613,
"color": {
"green": 237,
"blue": 153,
"red": 184
},
"score": 0.030864337
}
]
}
},
"gcs_url": "gs://vision-api-samples/image_properties_sample.jpg",
"timestamp": 1475666193.0
}