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import os
import math
import cv2
import numpy as np
import onnxruntime
from onnxruntime.capi import _pybind_state as C
import sys
class NudeDetector:
__labels = [
"FEMALE_GENITALIA_COVERED",
"FACE_FEMALE",
"BUTTOCKS_EXPOSED",
"FEMALE_BREAST_EXPOSED",
"FEMALE_GENITALIA_EXPOSED",
"MALE_BREAST_EXPOSED",
"ANUS_EXPOSED",
"FEET_EXPOSED",
"BELLY_COVERED",
"FEET_COVERED",
"ARMPITS_COVERED",
"ARMPITS_EXPOSED",
"FACE_MALE",
"BELLY_EXPOSED",
"MALE_GENITALIA_EXPOSED",
"ANUS_COVERED",
"FEMALE_BREAST_COVERED",
"BUTTOCKS_COVERED",
]
__nude_labels = [
"BUTTOCKS_EXPOSED",
"ANUS_EXPOSED",
"FEMALE_BREAST_EXPOSED",
"FEMALE_GENITALIA_EXPOSED",
"MALE_GENITALIA_EXPOSED",
"BELLY_EXPOSED"
]
def __init__(self, image=None, providers=None):
self.image = image
self.onnx_session = onnxruntime.InferenceSession(
os.path.join(os.path.dirname(__file__), "best.onnx"),
providers=C.get_available_providers() if not providers else providers,
)
model_inputs = self.onnx_session.get_inputs()
input_shape = model_inputs[0].shape
self.input_width = input_shape[2] # 320
self.input_height = input_shape[3] # 320
self.input_name = model_inputs[0].name
if image is not None:
self.detections = self.detect()
def set_image(self, image):
self.image = image
self.detections = self.detect()
def process_image(self, target_size=320):
img = self.image
img_height, img_width = img.shape[:2]
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
aspect = img_width / img_height
if img_height > img_width:
new_height = target_size
new_width = int(round(target_size * aspect))
else:
new_width = target_size
new_height = int(round(target_size / aspect))
resize_factor = math.sqrt(
(img_width ** 2 + img_height ** 2) / (new_width ** 2 + new_height ** 2)
)
img = cv2.resize(img, (new_width, new_height))
pad_x = target_size - new_width
pad_y = target_size - new_height
pad_top, pad_bottom = [int(i) for i in np.floor([pad_y, pad_y]) / 2]
pad_left, pad_right = [int(i) for i in np.floor([pad_x, pad_x]) / 2]
img = cv2.copyMakeBorder(
img,
pad_top,
pad_bottom,
pad_left,
pad_right,
cv2.BORDER_CONSTANT,
value=[0, 0, 0],
)
img = cv2.resize(img, (target_size, target_size))
image_data = img.astype("float32") / 255.0 # normalize
image_data = np.transpose(image_data, (2, 0, 1))
image_data = np.expand_dims(image_data, axis=0)
return image_data, resize_factor, pad_left, pad_top
def process_detections(self, output, resize_factor, pad_left, pad_top):
outputs = np.transpose(np.squeeze(output[0]))
rows = outputs.shape[0]
boxes = []
scores = []
class_ids = []
for i in range(rows):
classes_scores = outputs[i][4:]
max_score = np.amax(classes_scores)
if max_score >= 0.2:
class_id = np.argmax(classes_scores)
x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3]
left = int(round((x - w * 0.5 - pad_left) * resize_factor))
top = int(round((y - h * 0.5 - pad_top) * resize_factor))
width = int(round(w * resize_factor))
height = int(round(h * resize_factor))
class_ids.append(class_id)
scores.append(max_score)
boxes.append([left, top, width, height])
indices = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45)
detections = []
for i in indices:
box = boxes[i]
score = scores[i]
class_id = class_ids[i]
detections.append(
{"class": self.__labels[class_id], "score": float(score), "box": box}
)
return detections
def detect(self):
if self.image is None:
print(
"Error: Please provide an image either during initialization NudeDetector or by using the set_image method after initializing.")
sys.exit(1)
preprocessed_image, resize_factor, pad_left, pad_top = self.process_image(
self.input_width
)
outputs = self.onnx_session.run(None, {self.input_name: preprocessed_image})
detections = self.process_detections(outputs, resize_factor, pad_left, pad_top)
return detections
def visualize(self, parmanent=False, classes=None):
if classes is None:
classes = self.__nude_labels
detections = self.detections
if classes:
detections = [detection for detection in detections if detection["class"] in classes]
if parmanent:
img = self.image
else:
img = self.image.copy()
for detection in detections:
box = detection["box"]
x, y, w, h = box[0], box[1], box[2], box[3]
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
label = f"{detection['class']} ({round(detection['score'], 2)})"
cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
return img
def blur_nudity(self, parmanent=False, nude_classes=None):
if nude_classes is None:
nude_classes = self.__nude_labels
detections = self.detections
if parmanent:
img = self.image
else:
img = self.image.copy()
for detection in detections:
if detection["class"] in nude_classes:
box = detection["box"]
x, y, w, h = box[0], box[1], box[2], box[3]
blurred_area = img[y:y + h, x:x + w]
blurred_area = cv2.GaussianBlur(blurred_area, (75, 75), 100)
img[y:y + h, x:x + w] = blurred_area
return img
def is_nude(self, nude_classes=None, threshold=0.7):
if nude_classes is None:
nude_classes = self.__nude_labels
detections = self.detections
is_nude = False
for detection in detections:
if detection["class"] in nude_classes and detection["score"] >= threshold:
is_nude = True
return is_nude
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