Images
Make predictions on image inputs
Via URL
To get predictions for an input, you need to supply an image and the model you'd like to get predictions from. You can supply an image either with a publicly accessible URL or by directly sending bytes. You can send up to 128 images in one API call. You specify the model you'd like to use with the {model-id}
parameter.
Below is an example of how you would send image URLs and receive back predictions from the general
model.
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
url="https://samples.clarifai.com/metro-north.jpg"
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print("There was an error with your request!")
print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
print("\tDetails: {}".format(post_model_outputs_response.outputs[0].status.details))
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]
print("Predicted concepts:")
for concept in output.data.concepts:
print("%s %.2f" % (concept.name, concept.value))
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "ea68cac87c304b28a8046557062f34a0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:50:25Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "ea68cac87c304b28a8046557062f34a0",
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_HLmqFqBf",
"name": "train",
"app_id": null,
"value": 0.9989112
},
{
"id": "ai_fvlBqXZR",
"name": "railway",
"app_id": null,
"value": 0.9975532
},
{
"id": "ai_Xxjc3MhT",
"name": "transportation system",
"app_id": null,
"value": 0.9959158
},
{
"id": "ai_6kTjGfF6",
"name": "station",
"app_id": null,
"value": 0.992573
},
{
"id": "ai_RRXLczch",
"name": "locomotive",
"app_id": null,
"value": 0.992556
},
{
"id": "ai_VRmbGVWh",
"name": "travel",
"app_id": null,
"value": 0.98789215
},
{
"id": "ai_SHNDcmJ3",
"name": "subway system",
"app_id": null,
"value": 0.9816359
},
{
"id": "ai_jlb9q33b",
"name": "commuter",
"app_id": null,
"value": 0.9712483
},
{
"id": "ai_46lGZ4Gm",
"name": "railroad track",
"app_id": null,
"value": 0.9690325
},
{
"id": "ai_tr0MBp64",
"name": "traffic",
"app_id": null,
"value": 0.9687052
},
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9667078
},
{
"id": "ai_2gkfMDsM",
"name": "platform",
"app_id": null,
"value": 0.9624243
},
{
"id": "ai_CpFBRWzD",
"name": "urban",
"app_id": null,
"value": 0.960752
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.95864904
},
{
"id": "ai_6lhccv44",
"name": "business",
"app_id": null,
"value": 0.95720303
},
{
"id": "ai_971KsJkn",
"name": "track",
"app_id": null,
"value": 0.9494642
},
{
"id": "ai_WBQfVV0p",
"name": "city",
"app_id": null,
"value": 0.94089437
},
{
"id": "ai_dSCKh8xv",
"name": "fast",
"app_id": null,
"value": 0.9399334
},
{
"id": "ai_TZ3C79C6",
"name": "road",
"app_id": null,
"value": 0.93121606
},
{
"id": "ai_VSVscs9k",
"name": "terminal",
"app_id": null,
"value": 0.9230834
}
]
}
}
]
}
Via bytes
Below is an example of how you would send the bytes of an image and receive back predictions from the general
model.
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
file_bytes = f.read()
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
base64=file_bytes
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print("There was an error with your request!")
print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
print("\tDetails: {}".format(post_model_outputs_response.outputs[0].status.details))
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]
print("Predicted concepts:")
for concept in output.data.concepts:
print("%s %.2f" % (concept.name, concept.value))
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:59:23Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"data": {
"image": {
"url": "https://s3.amazonaws.com/clarifai-api/img/prod/b749af061d564b829fb816215f6dc832/e11c81745d6d42a78ef712236023df1c.jpeg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9973569
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.98865616
},
{
"id": "ai_JBPqff8z",
"name": "art",
"app_id": null,
"value": 0.986006
},
{
"id": "ai_5rD7vW4j",
"name": "wallpaper",
"app_id": null,
"value": 0.9722556
},
{
"id": "ai_sTjX6dqC",
"name": "abstract",
"app_id": null,
"value": 0.96476805
},
{
"id": "ai_Dm5GLXnB",
"name": "illustration",
"app_id": null,
"value": 0.922542
},
{
"id": "ai_5xjvC0Tj",
"name": "background",
"app_id": null,
"value": 0.8775655
},
{
"id": "ai_tBcWlsCp",
"name": "nature",
"app_id": null,
"value": 0.87474406
},
{
"id": "ai_rJGvwlP0",
"name": "insubstantial",
"app_id": null,
"value": 0.8196385
},
{
"id": "ai_2Bh4VMrb",
"name": "artistic",
"app_id": null,
"value": 0.8142488
},
{
"id": "ai_mKzmkKDG",
"name": "Christmas",
"app_id": null,
"value": 0.7996079
},
{
"id": "ai_RQccV41p",
"name": "woman",
"app_id": null,
"value": 0.7955615
},
{
"id": "ai_20SCBBZ0",
"name": "vector",
"app_id": null,
"value": 0.7775099
},
{
"id": "ai_4sJLn6nX",
"name": "dark",
"app_id": null,
"value": 0.7715479
},
{
"id": "ai_5Kp5FMJw",
"name": "still life",
"app_id": null,
"value": 0.7657637
},
{
"id": "ai_LM64MDHs",
"name": "shining",
"app_id": null,
"value": 0.7542407
},
{
"id": "ai_swtdphX8",
"name": "love",
"app_id": null,
"value": 0.74926054
},
{
"id": "ai_h45ZTxZl",
"name": "square",
"app_id": null,
"value": 0.7449074
},
{
"id": "ai_cMfj16kJ",
"name": "design",
"app_id": null,
"value": 0.73926914
},
{
"id": "ai_LxrzLJmf",
"name": "bright",
"app_id": null,
"value": 0.73790145
}
]
}
}
]
}
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