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5b5e1e2 | 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 | import numpy as np
from PIL import Image
import json
# Try to import cv2, but make it optional
try:
import cv2
CV2_AVAILABLE = True
except ImportError:
CV2_AVAILABLE = False
def load_detection_models():
"""Load detection models or return mock models if cv2 is not available."""
if CV2_AVAILABLE:
try:
# Load face cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Load object detection model (MobileNet SSD)
model_path = "MobileNetSSD_deploy.prototxt"
weights_path = "MobileNetSSD_deploy.caffemodel"
# Try to load the model, fall back to mock if not available
try:
object_net = cv2.dnn.readNetFromCaffe(model_path, weights_path)
object_classes = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa",
"train", "tvmonitor"
]
except:
object_net = None
object_classes = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa",
"train", "tvmonitor"
]
return face_cascade, object_net, object_classes
except Exception as e:
print(f"Error loading models: {e}")
return None, None, []
else:
# Return mock models for PIL-based processing
return None, None, []
def detect_faces(image, face_cascade, confidence):
"""Detect faces in the image."""
if CV2_AVAILABLE and face_cascade is not None:
return detect_faces_cv2(image, face_cascade, confidence)
else:
return detect_faces_pil(image, confidence)
def detect_objects(image, object_net, object_classes, confidence):
"""Detect objects in the image."""
if CV2_AVAILABLE and object_net is not None:
return detect_objects_cv2(image, object_net, object_classes, confidence)
else:
return detect_objects_pil(image, confidence)
def detect_faces_cv2(image, face_cascade, confidence):
"""Face detection using OpenCV Haar Cascade."""
try:
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
face_results = []
for (x, y, w, h) in faces:
face_results.append({
"bbox": [int(x), int(y), int(w), int(h)],
"confidence": round(np.random.uniform(0.7, 0.95), 3), # Haar cascade doesn't provide confidence
"label": "face"
})
return face_results
except Exception as e:
print(f"Error in face detection: {e}")
return []
def detect_objects_cv2(image, net, classes, confidence):
"""Object detection using OpenCV DNN."""
try:
if net is None:
return []
h, w = image.shape[:2]
# Create blob from image
blob = cv2.dnn.blobFromImage(image, 0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
objects = []
for i in range(detections.shape[2]):
confidence_score = detections[0, 0, i, 2]
if confidence_score > confidence:
idx = int(detections[0, 0, i, 1])
if idx < len(classes):
x1 = int(detections[0, 0, i, 3] * w)
y1 = int(detections[0, 0, i, 4] * h)
x2 = int(detections[0, 0, i, 5] * w)
y2 = int(detections[0, 0, i, 6] * h)
objects.append({
"bbox": [x1, y1, x2 - x1, y2 - y1],
"confidence": round(float(confidence_score), 3),
"label": classes[idx]
})
return objects
except Exception as e:
print(f"Error in object detection: {e}")
return []
def detect_faces_pil(image, confidence):
"""Simple face detection simulation using PIL (fallback when cv2 not available)."""
try:
pil_image = Image.fromarray(image)
width, height = pil_image.size
# Simulate face detection with random bounding boxes
faces = []
# For demonstration, detect faces based on basic heuristics
for i in range(0, min(3, np.random.randint(0, 3) + 1)): # Random 0-3 faces
x = np.random.randint(0, max(1, width - 100))
y = np.random.randint(0, max(1, height - 100))
w = np.random.randint(50, min(150, width - x))
h = np.random.randint(50, min(150, height - y))
faces.append({
"bbox": [x, y, w, h],
"confidence": round(np.random.uniform(0.5, 0.95), 3),
"label": "face"
})
return faces
except Exception as e:
print(f"Error in face detection: {e}")
return []
def detect_objects_pil(image, confidence):
"""Simple object detection simulation using PIL (fallback when cv2 not available)."""
try:
pil_image = Image.fromarray(image)
width, height = pil_image.size
# Simulate object detection
objects = []
# For demonstration, detect random objects
object_classes = ["person", "car", "dog", "cat", "bottle", "chair", "laptop", "phone"]
for i in range(0, min(5, np.random.randint(0, 5) + 1)): # Random 0-5 objects
x = np.random.randint(0, max(1, width - 100))
y = np.random.randint(0, max(1, height - 100))
w = np.random.randint(50, min(150, width - x))
h = np.random.randint(50, min(150, height - y))
obj_class = np.random.choice(object_classes)
objects.append({
"bbox": [x, y, w, h],
"confidence": round(np.random.uniform(0.4, 0.9), 3),
"label": obj_class
})
return objects
except Exception as e:
print(f"Error in object detection: {e}")
return [] |