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Soham Chandratre
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49ebad1
1
Parent(s):
59c748e
minor changes
Browse files
model/__pycache__/pothole_model.cpython-311.pyc
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Binary files a/model/__pycache__/pothole_model.cpython-311.pyc and b/model/__pycache__/pothole_model.cpython-311.pyc differ
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model/pothole_model.py
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from ultralyticsplus import YOLO, render_result
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from PIL import Image
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from io import BytesIO
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import numpy as np
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def load_model(image):
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# image_bytes = image.content
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model = YOLO('keremberke/yolov8n-pothole-segmentation')
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model.overrides['conf'] = 0.25
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model.overrides['iou'] = 0.45
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model.overrides['agnostic_nms'] = False
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model.overrides['max_det'] = 1000
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# Load image using PIL
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image = Image.open(BytesIO(image))
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image_array = np.array(image)
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# pil_image = pil_image.convert("RGB") # Ensure image is in RGB format
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# Convert PIL image to bytes
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# with io.BytesIO() as output:
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# pil_image.save(output, format='JPEG')
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# image_bytes = output.getvalue()
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results = model.predict(image_array)
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for result in results:
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boxes = result.boxes.xyxy
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conf = result.boxes.conf
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cls = result.boxes.cls
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obj_info = []
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for i, bbox in enumerate(boxes):
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label = result.names[int(cls[i])]
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obj_info.append({
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"Object": i+1,
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"Label": label,
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"Confidence": conf[i],
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"Bounding Box": bbox
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})
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render = render_result(model=model, image=image, result=results[0])
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if label:
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print(label)
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render.show()
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return label
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# from PIL import Image
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# from io import BytesIO
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# # Load model
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#
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# processor = AutoImageProcessor.from_pretrained("savioratharv/pothole_detection")
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# model = AutoModelForObjectDetection.from_pretrained("savioratharv/pothole_detection")
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# # Function to predict if an image contains a pothole
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# def predict_pothole(image_url):
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# return predicted_class
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# from PIL import Image
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# from io import BytesIO
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# from transformers import AutoImageProcessor, AutoModelForImageClassification
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# # Load model
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# processor = AutoImageProcessor.from_pretrained("taroii/pothole-detection-model")
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# model = AutoModelForImageClassification.from_pretrained("taroii/pothole-detection-model")
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# # Function to predict if an image contains a pothole
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# def predict_pothole(image_url):
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# return predicted_class
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from keras.models import load_model # TensorFlow is required for Keras to work
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from PIL import Image, ImageOps # Install pillow instead of PIL
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import numpy as np
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def load_image_model(image):
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# Disable scientific notation for clarity
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np.set_printoptions(suppress=True)
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# Load the model
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model = load_model("https://huggingface.co/spaces/Soham0708/pothole_detect/blob/main/keras_model.h5")
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# Load the labels
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class_names = open("labels.txt", "r").readlines()
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# Create the array of the right shape to feed into the keras model
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# The 'length' or number of images you can put into the array is
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# determined by the first position in the shape tuple, in this case 1
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data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
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# Replace this with the path to your image
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image = Image.open("<IMAGE_PATH>").convert("RGB")
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# resizing the image to be at least 224x224 and then cropping from the center
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size = (224, 224)
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image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
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# turn the image into a numpy array
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image_array = np.asarray(image)
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# Normalize the image
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normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
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# Load the image into the array
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data[0] = normalized_image_array
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# Predicts the model
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prediction = model.predict(data)
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index = np.argmax(prediction)
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class_name = class_names[index]
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confidence_score = prediction[0][index]
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# Print prediction and confidence score
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print("Class:", class_name[2:], end="")
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print("Confidence Score:", confidence_score)
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routes/__pycache__/route.cpython-311.pyc
CHANGED
Binary files a/routes/__pycache__/route.cpython-311.pyc and b/routes/__pycache__/route.cpython-311.pyc differ
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routes/route.py
CHANGED
@@ -1,7 +1,7 @@
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from fastapi import APIRouter, HTTPException,Depends,File, UploadFile
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from fastapi.responses import JSONResponse
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from config.database import admin_collection, user_collection,notification_collection,pothole_image_collection
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from model.pothole_model import
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from utils.auth import create_access_token, hash_password, verify_password, verify_token
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from schema.model import Admin, PoholeInfo, PotInfoById, PotholeFilters, PotholeModel, UpdatePotholeInfo, User, UserLogin, VerifyOtp
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image_bytes = response.content
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# Pass image bytes to your model function
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results =
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# if results == 1:
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# return JSONResponse(content={"response": "Pothole"})
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from fastapi import APIRouter, HTTPException,Depends,File, UploadFile
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from fastapi.responses import JSONResponse
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from config.database import admin_collection, user_collection,notification_collection,pothole_image_collection
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from model.pothole_model import load_image_model
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from utils.auth import create_access_token, hash_password, verify_password, verify_token
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from schema.model import Admin, PoholeInfo, PotInfoById, PotholeFilters, PotholeModel, UpdatePotholeInfo, User, UserLogin, VerifyOtp
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image_bytes = response.content
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# Pass image bytes to your model function
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results = load_image_model(image_bytes)
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# if results == 1:
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# return JSONResponse(content={"response": "Pothole"})
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