Merge develop to main

.gitignore

@@ -5,4 +5,6 @@
 .pytest_cache
 dist
 build
-fastlabel.egg-info
+fastlabel.egg-info
+test*.py
+annotation.json

Makefile

@@ -0,0 +1,7 @@
+all: black isort
+
+black:
+	black .
+
+isort:
+	isort .

README.md

@@ -23,30 +23,159 @@ import fastlabel
 client = fastlabel.Client()
 ```
 
-## Model Analysis
+## Limitation
 
-### Upload Predictions
+API is allowed to call 5000 times per hour. If you create/delete a large size of tasks, please wait a second for every requests.
+
+## Task
+
+### Create Task
+
+- Create a new task.
 
 ```python
-import fastlabel
+task = client.create_image_task(
+    project_id="YOUR_PROJECT_ID",
+    key="sample.jpg",
+    url="https://sample.com/sample.jpg"
+)
+```
 
-# Initialize client
-client = fastlabel.Client()
+- Create a new task with pre-defined labels. (Class should be configured on your project in advance)
+
+```python
+task = client.create_image_task(
+    project_id="YOUR_PROJECT_ID",
+    key="sample.jpg",
+    url="https://sample.com/sample.jpg",
+    labels=[
+        {
+            "type": "bbox",
+            "value": "bbox",
+            "points": [
+                { "x": 100, "y": 100},  # top-left
+                { "x": 200, "y": 200}   # bottom-right
+            ]
+        }
+    ]
+)
+```
 
-# Create predictions
+> Check [examples/create_image_task.py](/examples/create_image_task.py) for other label types, such as line, keyPoint and polygon.
+
+### Find Task
+
+- Find a single task.
+
+```python
+task = client.find_task(task_id="YOUR_TASK_ID")
+```
+
+### Get Tasks
+
+- Get tasks. (Up to 100 tasks)
+
+```python
+tasks = client.get_tasks(project_id="YOUR_PROJECT_ID")
+```
+
+- Filter and Get tasks. (Up to 100 tasks)
+
+```python
+tasks = client.get_tasks(
+    project_id="YOUR_PROJECT_ID",
+    status="submitted", # status can be 'registered', 'registered', 'submitted' or 'skipped'
+    review_status="accepted" # review_status can be 'notReviewed', 'inProgress', 'accepted' or 'declined'
+)
+```
+
+- Get a large size of tasks. (Over 100 tasks)
+
+```python
+import time
+
+# Iterate pages until new tasks are empty.
+all_tasks = []
+start_after = None
+while True:
+    time.sleep(1)
+
+    tasks = client.get_tasks(project_id="YOUR_PROJECT_ID", start_after=start_after)
+    all_tasks.extend(tasks)
+
+    if len(tasks) > 0:
+        start_after = tasks[-1]["id"] # Set the last task id to start_after
+    else:
+        break
+```
+
+> Please wait a second before sending another requests!
+
+### Delete Task
+
+```python
+client.delete_task(task_id="YOUR_TASK_ID")
+```
+
+### Task Response
+
+- Example of a single task object
+
+```python
+{
+    "id": "YOUR_TASK_ID",
+    "key": "sample.png",
+    "assigneeId": null,
+    "assigneeName": null,
+    "status": "registered",
+    "reviewAssigneeId": null,
+    "reviewAssigneeName": null,
+    "reviewStatus": "notReviewed",
+    "projectId": "YOUR_PROJECT_ID",
+    "datasetId": "YOUR_DATASET_ID",
+    "labels": [
+        {
+            "id": "YOUR_LABEL_ID",
+            "type": "bbox",
+            "value": "window",
+            "title": "窓",
+            "color": "#d9713e",
+            "metadata": [],
+            "points": [
+                { "x": 100, "y": 100},  # top-left
+                { "x": 200, "y": 200}   # bottom-right
+            ]
+        }
+    ],
+    "duration": 0,
+    "image": {
+        "width": 1500,
+        "height": 1200
+    },
+    "createdAt": "2020-12-25T15:02:00.513",
+    "updatedAt": "2020-12-25T15:02:00.513"
+}
+```
+
+## Model Analysis
+
+### Upload Predictions
+
+```python
+# Create your model predictions
 predictions = [
     {
-        "fileKey": "sample1.jpg",  # file name exists in your project
+        "fileKey": "sample.jpg",  # file name exists in your project
         "labels": [
             {
-                "value": "line_a",  # class value exists in your project
+                "value": "bbox_a",  # class value exists in your project
                 "points": [
-                    { "x": 10, "y": 10 },
-                    { "x": 20, "y": 20 },
+                    { "x": 10, "y": 10 },   # top-left
+                    { "x": 20, "y": 20 },   # botom-right
                 ]
             },
             {
-                "value": "line_b",
+                "value": "bbox_b",
                 "points": [
                     { "x": 30, "y": 30 },
                     { "x": 40, "y": 40 },
@@ -58,9 +187,9 @@ predictions = [
 
 # Upload predictions
 client.upload_predictions(
-    project_id="project_id",    # your fastlabel project id
-    analysis_type="line",    # annotation type to be analyze ("bbox" or "line" are supported)
-    threshold=25,   # IoU percentage/pixel to analyze labels. (Ex: 0 - 100)
+    project_id="YOUR_PROJECT_ID",    # your fastlabel project id
+    analysis_type="bbox",    # annotation type to be analyze (Only "bbox" or "line" are supported)
+    threshold=80,   # IoU percentage/pixel distance to check labels are correct. (Ex: 0 - 100)
     predictions=predictions
 )
 ```

contributing/README.md

@@ -0,0 +1,22 @@
+# Contributing Guideline
+
+## Pull Request Checklist
+
+Before sending your pull requests, make sure you followed this list.
+
+- Read Contributing Guideline
+- Run formatter and linter
+
+## Formatter and Linter
+
+### Installation
+
+```bash
+$ pip install -r contributing/requirements.txt
+```
+
+### Run Formatter and Linter
+
+```bash
+make all
+```

contributing/requirements.txt

@@ -0,0 +1,3 @@
+black==20.8b1
+flake8==3.8.4
+isort==5.6.4

examples/create_image_task.py

@@ -0,0 +1,61 @@
+from pprint import pprint
+
+import fastlabel
+
+# Initialize client
+client = fastlabel.Client()
+
+project_id = "YOUR_PROJECT_ID"
+key = "YOUR_IMAGE_KEY"  # Should be an unique in your project
+url = "YOUR_IMAGE_URL"
+labels = [
+    {
+        "type": "bbox",
+        "value": "bbox",
+        "points": [
+            {"x": 100, "y": 100},  # top-left
+            {"x": 200, "y": 200},  # bottom-right
+        ],
+    },
+    {
+        "type": "line",
+        "value": "line",
+        "points": [{"x": 200, "y": 200}, {"x": 250, "y": 250}],
+    },
+    {"type": "keyPoint", "value": "keyPoint", "points": {"x": 10, "y": 10}},
+    {
+        "type": "polygon",
+        "value": "polygon",
+        "points": [
+            {"x": 300, "y": 300},
+            {"x": 320, "y": 320},
+            {"x": 340, "y": 220},
+            {"x": 310, "y": 200},
+        ],
+    },
+    {
+        "type": "polyline",
+        "value": "polyline",
+        "points": [
+            {"x": 100, "y": 300},
+            {"x": 120, "y": 320},
+            {"x": 140, "y": 220},
+            {"x": 110, "y": 200},
+        ],
+    },
+    {
+        "type": "segmentation",
+        "value": "segmentation",
+        "points": [
+            [
+                {"x": 400, "y": 400},
+                {"x": 420, "y": 420},
+                {"x": 440, "y": 420},
+                {"x": 410, "y": 400},
+            ]
+        ],
+    },
+]
+
+task = client.create_image_task(project_id=project_id, key=key, url=url, labels=labels)
+pprint(task)