Upload 2 files
Browse files- README.md +17 -9
- WaterFlowCountersRecognition.py +36 -12
README.md
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@@ -1,16 +1,24 @@
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---
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dataset_info:
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features:
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-
- name:
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dtype: string
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- name: image
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dtype: image
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- name:
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sequence:
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- name:
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sequence: int64
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- name: all_points_y
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sequence: int64
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- name: name
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dtype:
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class_label:
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@@ -29,11 +37,11 @@ dataset_info:
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config_name: WFCR_full
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splits:
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- name: train
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-
num_bytes:
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num_examples: 1644
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- name: test
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num_bytes:
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num_examples: 412
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download_size:
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dataset_size:
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---
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---
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dataset_info:
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features:
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+
- name: id
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dtype: string
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- name: image
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dtype: image
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- name: width
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dtype: int32
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- name: height
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dtype: int32
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- name: annotations
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sequence:
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- name: bbox
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sequence: int64
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length: 4
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- name: area
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dtype: int64
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- name: segmentation
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sequence:
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sequence: int64
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- name: name
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dtype:
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class_label:
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config_name: WFCR_full
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splits:
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- name: train
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num_bytes: 937884
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num_examples: 1644
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- name: test
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num_bytes: 239710
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num_examples: 412
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download_size: 46791554
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dataset_size: 1177594
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---
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WaterFlowCountersRecognition.py
CHANGED
@@ -1,5 +1,6 @@
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import json
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import os
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import datasets
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@@ -62,10 +63,13 @@ class WaterFlowCounter(datasets.GeneratorBasedBuilder):
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{
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"id": datasets.Value("string"),
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"image": datasets.Image(),
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"
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{
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"
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"
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"name": datasets.ClassLabel(names=_REGION_NAME, num_classes=3),
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"rotated": datasets.ClassLabel(names=_REGION_ROTETION, num_classes=4)
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}
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@@ -118,13 +122,16 @@ class WaterFlowCounter(datasets.GeneratorBasedBuilder):
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for file in os.listdir(folder_dir):
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filepath = os.path.join(folder_dir, file)
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with open(filepath, "rb") as f:
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image_bytes = f.read()
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#print(image_bytes)
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names = []
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rotated = []
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if annotations['_via_img_metadata'][el]['filename'] == file:
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for region in annotations['_via_img_metadata'][el]['regions']:
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all_x
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all_y
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for name in list(region['region_attributes']['name'].keys()):
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names.append(name_to_id[name])
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try:
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yield idx, {
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"id": file,
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"image": {"path": filepath, "bytes": image_bytes},
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"
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"name":names,
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"rotated": rotated
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}
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import json
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import os
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from PIL import Image
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import datasets
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{
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"id": datasets.Value("string"),
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"image": datasets.Image(),
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"width": datasets.Value('int32'),
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"height": datasets.Value('int32'),
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"annotations": datasets.Sequence(
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{
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"bbox": datasets.Sequence(datasets.Value("int64"), length=4),
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"area": datasets.Value("int64"),
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"segmentation": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
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"name": datasets.ClassLabel(names=_REGION_NAME, num_classes=3),
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"rotated": datasets.ClassLabel(names=_REGION_ROTETION, num_classes=4)
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}
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for file in os.listdir(folder_dir):
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filepath = os.path.join(folder_dir, file)
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with open(filepath, "rb") as f:
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image_bytes = f.read()
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image = Image.open(filepath)
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width, height = image.size
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all_bbox = []
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all_area = []
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all_segmentation = []
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names = []
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rotated = []
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if annotations['_via_img_metadata'][el]['filename'] == file:
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for region in annotations['_via_img_metadata'][el]['regions']:
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all_x = region['shape_attributes']['all_points_x']
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all_y = region['shape_attributes']['all_points_y']
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x_min = min(all_x)
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y_min = min(all_y)
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x_max = max(all_x)
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y_max = max(all_y)
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p_width = x_max - x_min
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p_height = y_max - y_min
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bbox = [x_min, y_min, p_width, p_height ]
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area = p_width * p_height
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segmentation = list(zip(all_x, all_y))
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all_bbox.append(bbox)
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all_area.append(area)
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all_segmentation.append(segmentation)
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for name in list(region['region_attributes']['name'].keys()):
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names.append(name_to_id[name])
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try:
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yield idx, {
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"id": file,
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"image": {"path": filepath, "bytes": image_bytes},
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"width": width,
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"height": height,
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"annotations": {
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"area": all_area,
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"bbox": all_bbox,
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"segmentation": all_segmentation,
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"name":names,
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"rotated": rotated
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}
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