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d32e728 | 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 | import sys
from pathlib import Path
import numpy as np
import requests
from io import BytesIO
from PIL import Image
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from notifications.serializers import ImageClassifierSerializer, ClassificationResultSerializer
import torch
import torch.nn as nn
from torchvision import transforms
import tensorflow as tf
# PATHS
BASE_DIR = Path(__file__).resolve().parent.parent
PYTORCH_PATH = BASE_DIR / "models" / "pytorch_model.pth"
TENSORFLOW_PATH = BASE_DIR / "models" / "model_best.keras"
CLASSES = ["buildings", "forest", "glacier", "mountain", "sea", "street"]
CONFIDENCE_THRESHOLD = 0.6
# PYTORCH MODEL
class CNN(nn.Module):
def __init__(self, num_classes=6):
super().__init__()
self.block1 = self._block(3, 32)
self.block2 = self._block(32, 64)
self.block3 = self._block(64, 128)
self.block4 = self._block(128, 256)
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Linear(256, 128)
self.fc2 = nn.Linear(128, num_classes)
self.dropout = nn.Dropout(0.5)
def _block(self, in_c, out_c):
return nn.Sequential(
nn.Conv2d(in_c, out_c, 3, padding=1),
nn.BatchNorm2d(out_c),
nn.ReLU(),
nn.Conv2d(out_c, out_c, 3, padding=1),
nn.BatchNorm2d(out_c),
nn.ReLU(),
nn.MaxPool2d(2)
)
def forward(self, x):
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.gap(x)
x = x.view(x.size(0), -1)
x = self.dropout(torch.relu(self.fc1(x)))
x = self.fc2(x)
return x
# TRANSFORM
pytorch_transform = transforms.Compose([
transforms.Resize((150, 150)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
_pytorch_model = None
_tensorflow_model = None
def get_pytorch_model():
global _pytorch_model
if _pytorch_model is None:
model = CNN(num_classes=len(CLASSES))
model.load_state_dict(torch.load(str(PYTORCH_PATH), map_location="cpu"))
model.eval()
_pytorch_model = model
return _pytorch_model
def get_tensorflow_model():
global _tensorflow_model
if _tensorflow_model is None:
_tensorflow_model = tf.keras.models.load_model(str(TENSORFLOW_PATH), compile=False)
return _tensorflow_model
class ImageClassifierAPIView(APIView):
def post(self, request):
serializer = ImageClassifierSerializer(data=request.data)
if not serializer.is_valid():
return Response(serializer.errors, status=400)
try:
if "image" in serializer.validated_data:
image = Image.open(serializer.validated_data["image"]).convert("RGB")
else:
image_url = serializer.validated_data["image_url"]
if not image_url.startswith("http"):
return Response({"error": "Invalid URL"}, status=400)
resp = requests.get(image_url, timeout=10)
resp.raise_for_status()
image = Image.open(BytesIO(resp.content)).convert("RGB")
model_name = serializer.validated_data.get("model", "pytorch")
if model_name == "pytorch":
result = self._predict_pytorch(image)
else:
result = self._predict_tensorflow(image)
result["model_used"] = model_name
return Response(result, status=200)
except Exception as e:
print("ERROR:", e)
return Response({"error": str(e)}, status=400)
def _predict_pytorch(self, image):
model = get_pytorch_model()
tensor = pytorch_transform(image).unsqueeze(0)
with torch.no_grad():
outputs = model(tensor)
probs = torch.nn.functional.softmax(outputs, dim=1)
values, indices = torch.topk(probs, k=len(CLASSES), dim=1)
values = values.squeeze().cpu().numpy()
indices = indices.squeeze().cpu().numpy()
confidence = float(values[0])
all_probs = [
{"class": CLASSES[indices[i]], "probability": float(values[i])}
for i in range(len(CLASSES))
]
if confidence < CONFIDENCE_THRESHOLD:
return {
"predicted_class": "unknown",
"confidence": confidence,
"all_probabilities": all_probs
}
return {
"predicted_class": CLASSES[indices[0]],
"confidence": confidence,
"all_probabilities": all_probs
}
# TENSORFLOW
def _predict_tensorflow(self, image):
model = get_tensorflow_model()
img = image.resize((130, 130))
arr = np.array(img, dtype=np.float32)
arr = arr[:, :, ::-1]
arr = np.expand_dims(arr, 0)
preds = model.predict(arr, verbose=0)[0]
sorted_idx = np.argsort(preds)[::-1]
confidence = float(preds[sorted_idx[0]])
all_probs = [
{"class": CLASSES[i], "probability": float(preds[i])}
for i in sorted_idx
]
if confidence < CONFIDENCE_THRESHOLD:
return {
"predicted_class": "unknown",
"confidence": confidence,
"all_probabilities": all_probs
}
return {
"predicted_class": CLASSES[sorted_idx[0]],
"confidence": confidence,
"all_probabilities": all_probs
} |