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import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.models as models
from PIL import Image
from torchvision import models
from torchvision import transforms as T
from torchvision.ops import nms
from typing import List, Any, Tuple
STATE_DICT = os.path.join(
os.path.dirname(__file__), "..", "state_dicts", "signature_blocks_v14.pth"
)
def get_device():
if torch.cuda.is_available():
device = "cuda"
# aten::hardsigmoid.out' is not currently implemented for the MPS device
# setting fallback does not work either
# elif torch.backends.mps.is_built():
# device = "mps"
else:
device = "cpu"
return device
class ImgFactory:
def serialize(self, img: Any) -> Any:
serializer = self._get_serializer(img)
return serializer(img)
def _get_serializer(self, img: Any) -> Any:
if isinstance(img, str):
return self._serialize_string_to_image
else:
return self._serialize_image_to_image
def _serialize_string_to_image(self, img):
return Image.open(img)
def _serialize_image_to_image(self, img):
return img
class SignatureBlockModel(ImgFactory):
def __init__(self, img, state_dict_path=STATE_DICT):
self.state_dict_path = state_dict_path
self.classes = {0: "NOTHING", 1: "SIGNED_BLOCK", 2: "UNSIGNED_BLOCK"}
self.n_classes = len(self.classes)
self.device = get_device()
self.model = self._load_model()
self.img = self.serialize(img)
with torch.no_grad():
self.model.eval()
self.predictions = self._get_prediction()
def _load_model(self):
weights = models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights.DEFAULT
model = models.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=weights)
# change the head
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = models.detection.faster_rcnn.FastRCNNPredictor(
in_features, self.n_classes
)
model.load_state_dict(
torch.load(self.state_dict_path, map_location=self.device)
)
return model.to(self.device)
def filter_overlap(self, predictions, iou_threshold=0.3):
boxes = predictions[0]["boxes"]
scores = predictions[0]["scores"]
nms_filter = nms(boxes=boxes, scores=scores, iou_threshold=iou_threshold)
return nms_filter
def filter_scores(self, predictions, score_thrs=0.94):
nms_filter = self.filter_overlap(predictions)
boxes = predictions[0]["boxes"]
scores = predictions[0]["scores"]
labels = predictions[0]["labels"]
score_filter = scores[nms_filter] > score_thrs
boxes = boxes[nms_filter][score_filter]
scores = scores[nms_filter][score_filter]
labels = labels[nms_filter][score_filter]
return boxes, scores, labels
def _get_prediction(self):
transform = T.Compose([T.ToTensor()])
img = transform(self.img)
img = img.to(self.device)
predictions = self.model([img])
boxes, scores, labels = self.filter_scores(predictions)
return [{"boxes": boxes, "scores": scores, "labels": labels}]
def get_boxes(self):
pred = self._get_prediction()
boxes = pred[0]["boxes"].cpu().detach().numpy()
int_boxes = []
for box in boxes:
box = [int(x) for x in box]
int_boxes.append(box)
return int_boxes
def get_scores(self):
pred = self._get_prediction()
scores = pred[0]["scores"].cpu().detach().numpy()
return scores
def get_labels(self):
pred = self._get_prediction()
labels = pred[0]["labels"].cpu().detach().numpy()
return labels
def get_labels_names(self):
pred = self._get_prediction()
labels = pred[0]["labels"].cpu().detach().numpy()
label_names = [self.classes[label] for label in labels]
return label_names
def _get_prediction_dict(self):
boxes = self.get_boxes()
scores = self.get_scores()
labels = self.get_labels()
return {"boxes": boxes, "scores": scores, "labels": labels}
def _signature_crops(self, show=True):
boxes = self.get_boxes()
scores = self.get_scores()
labels = self.get_labels()
signature_crops = []
for box, label, score in tuple(zip(boxes, labels, scores)):
crop = self.extract_box(box)
if show:
crop = plt.imshow(crop)
signature_crops.append(crop)
return signature_crops
def get_prediction(self):
return self._get_prediction_dict()
def get_image(self):
return self.img
def get_image_array(self):
return np.array(self.img)
def get_box_crops(self):
boxes = self.get_boxes()
box_crops = []
for box in boxes:
crop = self.img.crop(box)
box_crops.append(crop)
return box_crops
def extract_box(self, box):
xmin, ymin, xmax, ymax = box
image = np.array(self.img)
return image[ymin:ymax, xmin:xmax]
def show_boxes(self):
boxes = self.get_boxes()
scores = self.get_scores()
labels = self.get_labels()
box_crops = []
for box, label, score in tuple(zip(boxes, labels, scores)):
print(f"Status: {self.classes[label]}")
print(f"Score: {score}")
crop = self.extract_box(box)
plt.imshow(crop)
plt.show()
plt.close()
box_crops.append(crop)
return box_crops
def draw_boxes(self):
img = np.array(self.img)
boxes = self.get_boxes()
labels = self.get_labels()
thickness = 2
overlay = img.copy()
for box, label in zip(boxes, labels):
box = [int(x) for x in box]
if label == 2:
color = (0, 0, 255) # red
elif label == 1:
color = (0, 255, 0) # green
cv2.rectangle(
overlay, (box[0], box[1]), (box[2], box[3]), color, -1
) # Filled rectangle
alpha = 0.4 # Transparency factor
image_boxes = cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0)
# Draw box outlines
for box, label in zip(boxes, labels):
box = [int(x) for x in box]
if label == 2:
color = (0, 0, 255) # red
elif label == 1:
color = (0, 255, 0) # green
cv2.rectangle(
image_boxes, (box[0], box[1]), (box[2], box[3]), color, thickness
)
return Image.fromarray(cv2.cvtColor(image_boxes, cv2.COLOR_BGR2RGB))
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