File size: 7,080 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 |
from typing import Dict, Optional, Tuple
import cv2
import numpy as np
from tqdm.auto import tqdm
from ultralytics.engine.results import Results
from ultralytics.models.yolo.segment import SegmentationPredictor
from ultralytics.utils.ops import scale_image
import wandb
from wandb.integration.ultralytics.bbox_utils import (
get_ground_truth_bbox_annotations,
get_mean_confidence_map,
)
def instance_mask_to_semantic_mask(instance_mask, class_indices):
height, width, num_instances = instance_mask.shape
semantic_mask = np.zeros((height, width), dtype=np.uint8)
for i in range(num_instances):
instance_map = instance_mask[:, :, i]
class_index = class_indices[i]
semantic_mask[instance_map == 1] = class_index
return semantic_mask
def get_boxes_and_masks(result: Results) -> Tuple[Dict, Dict, Dict]:
boxes = result.boxes.xywh.long().numpy()
classes = result.boxes.cls.long().numpy()
confidence = result.boxes.conf.numpy()
class_id_to_label = {int(k): str(v) for k, v in result.names.items()}
class_id_to_label.update({len(result.names.items()): "background"})
mean_confidence_map = get_mean_confidence_map(
classes, confidence, class_id_to_label
)
masks = None
if result.masks is not None:
scaled_instance_mask = scale_image(
np.transpose(result.masks.data.numpy(), (1, 2, 0)),
result.orig_img[:, :, ::-1].shape,
)
scaled_semantic_mask = instance_mask_to_semantic_mask(
scaled_instance_mask, classes.tolist()
)
scaled_semantic_mask[scaled_semantic_mask == 0] = len(result.names.items())
masks = {
"predictions": {
"mask_data": scaled_semantic_mask,
"class_labels": class_id_to_label,
}
}
box_data, total_confidence = [], 0.0
for idx in range(len(boxes)):
box_data.append(
{
"position": {
"middle": [int(boxes[idx][0]), int(boxes[idx][1])],
"width": int(boxes[idx][2]),
"height": int(boxes[idx][3]),
},
"domain": "pixel",
"class_id": int(classes[idx]),
"box_caption": class_id_to_label[int(classes[idx])],
"scores": {"confidence": float(confidence[idx])},
}
)
total_confidence += float(confidence[idx])
boxes = {
"predictions": {
"box_data": box_data,
"class_labels": class_id_to_label,
},
}
return boxes, masks, mean_confidence_map
def plot_mask_predictions(
result: Results, model_name: str, table: Optional[wandb.Table] = None
) -> Tuple[wandb.Image, Dict, Dict, Dict]:
result = result.to("cpu")
boxes, masks, mean_confidence_map = get_boxes_and_masks(result)
image = wandb.Image(result.orig_img[:, :, ::-1], boxes=boxes, masks=masks)
if table is not None:
table.add_data(
model_name,
image,
len(boxes["predictions"]["box_data"]),
mean_confidence_map,
result.speed,
)
return table
return image, masks, boxes["predictions"], mean_confidence_map
def structure_prompts_and_image(image: np.array, prompt: Dict) -> Dict:
wb_box_data = []
if prompt["bboxes"] is not None:
wb_box_data.append(
{
"position": {
"middle": [prompt["bboxes"][0], prompt["bboxes"][1]],
"width": prompt["bboxes"][2],
"height": prompt["bboxes"][3],
},
"domain": "pixel",
"class_id": 1,
"box_caption": "Prompt-Box",
}
)
if prompt["points"] is not None:
image = image.copy().astype(np.uint8)
image = cv2.circle(
image, tuple(prompt["points"]), 5, (0, 255, 0), -1, lineType=cv2.LINE_AA
)
wb_box_data = {
"prompts": {
"box_data": wb_box_data,
"class_labels": {1: "Prompt-Box"},
}
}
return image, wb_box_data
def plot_sam_predictions(
result: Results, prompt: Dict, table: wandb.Table
) -> wandb.Table:
result = result.to("cpu")
image = result.orig_img[:, :, ::-1]
image, wb_box_data = structure_prompts_and_image(image, prompt)
image = wandb.Image(
image,
boxes=wb_box_data,
masks={
"predictions": {
"mask_data": np.squeeze(result.masks.data.cpu().numpy().astype(int)),
"class_labels": {0: "Background", 1: "Prediction"},
}
},
)
table.add_data(image)
return table
def plot_segmentation_validation_results(
dataloader,
class_label_map,
model_name: str,
predictor: SegmentationPredictor,
table: wandb.Table,
max_validation_batches: int,
epoch: Optional[int] = None,
):
data_idx = 0
num_dataloader_batches = len(dataloader.dataset) // dataloader.batch_size
max_validation_batches = min(max_validation_batches, num_dataloader_batches)
for batch_idx, batch in enumerate(dataloader):
prediction_results = predictor(batch["im_file"])
progress_bar_result_iterable = tqdm(
enumerate(prediction_results),
total=len(prediction_results),
desc=f"Generating Visualizations for batch-{batch_idx + 1}/{max_validation_batches}",
)
for img_idx, prediction_result in progress_bar_result_iterable:
prediction_result = prediction_result.to("cpu")
(
_,
prediction_mask_data,
prediction_box_data,
mean_confidence_map,
) = plot_mask_predictions(prediction_result, model_name)
try:
ground_truth_data = get_ground_truth_bbox_annotations(
img_idx, batch["im_file"][img_idx], batch, class_label_map
)
wandb_image = wandb.Image(
batch["im_file"][img_idx],
boxes={
"ground-truth": {
"box_data": ground_truth_data,
"class_labels": class_label_map,
},
"predictions": prediction_box_data,
},
masks=prediction_mask_data,
)
table_rows = [
data_idx,
batch_idx,
wandb_image,
mean_confidence_map,
prediction_result.speed,
]
table_rows = [epoch] + table_rows if epoch is not None else table_rows
table_rows = [model_name] + table_rows
table.add_data(*table_rows)
data_idx += 1
except TypeError:
pass
if batch_idx + 1 == max_validation_batches:
break
return table
|