Spaces:
Sleeping
Sleeping
File size: 10,853 Bytes
b78b0dc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 |
import streamlit as st
import numpy as np
import plotly.express as px
import cv2
from src.error_analysis import ErrorAnalysis, transform_gt_bbox_format
import yaml
import os
from src.confusion_matrix import ConfusionMatrix
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
def amend_cm_df(cm_df, labels_dict):
"""Helper function to amend the index and column name for readability
Example - index currently is 0, 1 ... -> GT - person
Likewise in Column - 0, 1 ... -> Pred - person etc
Args:
cm_df (_type_): _description_
labels_dict (_type_): _description_
Returns:
_type_: _description_
"""
index_list = list(labels_dict.values())
index_list.append("background")
cm_df = cm_df.set_axis([f"GT - {elem}" for elem in index_list])
cm_df = cm_df.set_axis([f"Pred - {elem}" for elem in index_list], axis=1)
cm_df = cm_df.astype(int)
return cm_df
class ImageTool:
def __init__(self, cfg_path="cfg/cfg.yml"):
# inistialising the model and getting the annotations
self.ea_obj = ErrorAnalysis(cfg_path)
cfg_file = open(cfg_path)
self.cfg_obj = yaml.load(cfg_file, Loader=yaml.FullLoader)
self.inference_folder = self.ea_obj.inference_folder
self.ea_obj.get_annots()
self.gt_annots = self.ea_obj.gt_dict
self.all_img = os.listdir(self.inference_folder)
# for labels
self.labels_dict = self.cfg_obj["error_analysis"]["labels_dict"]
self.labels_dict = {v: k for k, v in self.labels_dict.items()}
self.idx_base = self.cfg_obj["error_analysis"]["idx_base"]
# for visualisation
self.bbox_thickness = self.cfg_obj["visual_tool"]["bbox_thickness"]
self.font_scale = self.cfg_obj["visual_tool"]["font_scale"]
self.font_thickness = self.cfg_obj["visual_tool"]["font_thickness"]
self.pred_colour = tuple(self.cfg_obj["visual_tool"]["pred_colour"])
self.gt_colour = tuple(self.cfg_obj["visual_tool"]["gt_colour"])
def show_img(self, img_fname="000000011149.jpg", show_preds=False, show_gt=False):
"""_summary_
Args:
img_fname (str, optional): _description_. Defaults to "000000011149.jpg".
show_preds (bool, optional): _description_. Defaults to False.
show_gt (bool, optional): _description_. Defaults to False.
Returns:
_type_: _description_
"""
img = cv2.imread(f"{self.inference_folder}{img_fname}")
labels = {"x": "X", "y": "Y", "color": "Colour"}
if show_preds:
preds = self.get_preds(img_fname)
img = self.draw_pred_bboxes(img, preds)
if show_gt:
gt_annots = self.get_gt_annot(img_fname)
img = self.draw_gt_bboxes(img, gt_annots)
fig = px.imshow(img[..., ::-1], aspect="equal", labels=labels)
if show_gt and show_preds:
cm_df, cm_tpfpfn_dict = self.generate_cm_one_image(preds, gt_annots)
return [fig, cm_df, cm_tpfpfn_dict]
return fig
def show_img_sbs(self, img_fname="000000011149.jpg"):
"""_summary_
Args:
img_fname (str, optional): _description_. Defaults to "000000011149.jpg".
Returns:
_type_: _description_
"""
# shows the image side by side
img = cv2.imread(f"{self.inference_folder}{img_fname}")
labels = {"x": "X", "y": "Y", "color": "Colour"}
img_pred = img.copy()
img_gt = img.copy()
preds = self.get_preds(img_fname)
img_pred = self.draw_pred_bboxes(img_pred, preds)
gt_annots = self.get_gt_annot(img_fname)
img_gt = self.draw_gt_bboxes(img_gt, gt_annots)
fig1 = px.imshow(img_gt[..., ::-1], aspect="equal", labels=labels)
fig2 = px.imshow(img_pred[..., ::-1], aspect="equal", labels=labels)
fig2.update_yaxes(visible=False)
cm_df, cm_tpfpfn_df = self.generate_cm_one_image(preds, gt_annots)
return [fig1, fig2, cm_df, cm_tpfpfn_df]
def generate_cm_one_image(self, preds, gt_annots):
"""_summary_
Args:
preds (_type_): _description_
gt_annots (_type_): _description_
Returns:
_type_: _description_
"""
num_classes = len(list(self.cfg_obj["error_analysis"]["labels_dict"].keys()))
idx_base = self.cfg_obj["error_analysis"]["idx_base"]
conf_threshold, iou_threshold = (
self.ea_obj.model.score_threshold,
self.ea_obj.model.iou_threshold,
)
cm = ConfusionMatrix(
num_classes=num_classes,
CONF_THRESHOLD=conf_threshold,
IOU_THRESHOLD=iou_threshold,
)
gt_annots[:, 0] -= idx_base
preds[:, -1] -= idx_base
cm.process_batch(preds, gt_annots)
confusion_matrix_df = cm.return_as_df()
cm.get_tpfpfn()
cm_tpfpfn_dict = {
"True Positive": cm.tp,
"False Positive": cm.fp,
"False Negative": cm.fn,
}
cm_tpfpfn_df = pd.DataFrame(cm_tpfpfn_dict, index=[0])
cm_tpfpfn_df = cm_tpfpfn_df.set_axis(["Values"], axis=0)
cm_tpfpfn_df = cm_tpfpfn_df.astype(int)
# amend df
confusion_matrix_df = amend_cm_df(confusion_matrix_df, self.labels_dict)
# print (cm.matrix)
return confusion_matrix_df, cm_tpfpfn_df
def get_preds(self, img_fname="000000011149.jpg"):
"""_summary_
Args:
img_fname (str, optional): _description_. Defaults to "000000011149.jpg".
Returns:
_type_: _description_
"""
# run inference using the error analysis object per image
outputs, img_shape = self.ea_obj.generate_inference(img_fname)
# converts image coordinates from normalised to integer values
# image shape is [Y, X, C] (because Rows are Y)
# So don't get confused!
outputs[:, 0] *= img_shape[1]
outputs[:, 1] *= img_shape[0]
outputs[:, 2] *= img_shape[1]
outputs[:, 3] *= img_shape[0]
return outputs
def get_gt_annot(self, img_fname):
"""_summary_
Args:
img_fname (_type_): _description_
Returns:
_type_: _description_
"""
ground_truth = self.gt_annots[img_fname].copy()
img = cv2.imread(f"{self.inference_folder}{img_fname}")
img_shape = img.shape
ground_truth = transform_gt_bbox_format(ground_truth, img_shape, format="coco")
# converts image coordinates from normalised to integer values
# image shape is [Y, X, C] (because Rows are Y)
# So don't get confused!
ground_truth[:, 1] *= img_shape[1]
ground_truth[:, 2] *= img_shape[0]
ground_truth[:, 3] *= img_shape[1]
ground_truth[:, 4] *= img_shape[0]
return ground_truth
def draw_pred_bboxes(self, img_pred, preds):
"""_summary_
Args:
img_pred (_type_): _description_
preds (_type_): _description_
Returns:
_type_: _description_
"""
for pred in preds:
pred = pred.astype(int)
img_pred = cv2.rectangle(
img_pred,
(pred[0], pred[1]),
(pred[2], pred[3]),
color=self.pred_colour,
thickness=self.bbox_thickness,
)
img_pred = cv2.putText(
img_pred,
self.labels_dict[pred[5]],
(pred[0] + 5, pred[1] + 25),
color=self.pred_colour,
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=self.font_scale,
thickness=self.font_thickness,
)
return img_pred
def draw_gt_bboxes(self, img_gt, gt_annots, **kwargs):
"""_summary_
Args:
img_gt (_type_): _description_
gt_annots (_type_): _description_
Returns:
_type_: _description_
"""
for annot in gt_annots:
annot = annot.astype(int)
# print (annot)
img_gt = cv2.rectangle(
img_gt,
(annot[1], annot[2]),
(annot[3], annot[4]),
color=self.gt_colour,
thickness=self.bbox_thickness,
)
img_gt = cv2.putText(
img_gt,
self.labels_dict[annot[0]],
(annot[1] + 5, annot[2] + 25),
color=(0, 255, 0),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=self.font_scale,
thickness=self.font_thickness,
)
return img_gt
def plot_with_preds_gt(self, option, side_by_side=False, plot_type=None):
"""Rules on what plot to generate
Args:
option (_string_): image filename. Toggled on the app itself. See app.py
side_by_side (bool, optional): Whether to have two plots side by side.
Defaults to False.
plot_type (_type_, optional): "all" - both GT and pred will be plotted,
"pred" - only preds,
"GT" - only ground truth
None - only image generated
Will be overridden if side_by_side = True
Defaults to None.
"""
if plot_type == "all":
plot, df, cm_tpfpfn_df = self.show_img(
option, show_preds=True, show_gt=True
)
st.plotly_chart(plot, use_container_width=True)
st.caption("Blue: Model BBox, Green: GT BBox")
st.table(df)
st.table(cm_tpfpfn_df)
elif plot_type == "pred":
st.plotly_chart(
self.show_img(option, show_preds=True), use_container_width=True
)
elif plot_type == "gt":
st.plotly_chart(
self.show_img(option, show_gt=True), use_container_width=True
)
elif side_by_side:
plot1, plot2, df, cm_tpfpfn_df = self.show_img_sbs(option)
col1, col2 = st.columns(2)
with col1:
col1.subheader("Ground Truth")
st.plotly_chart(plot1, use_container_width=True)
with col2:
col2.subheader("Prediction")
st.plotly_chart(plot2, use_container_width=True)
st.table(df)
st.table(cm_tpfpfn_df)
else:
st.plotly_chart(self.show_img(option), use_container_width=True)
|