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Sleeping
Soham Chandratre
commited on
Commit
·
94c7394
1
Parent(s):
5e6c258
minor changes
Browse files- keras_model.h5 +3 -0
- labels.txt +2 -0
- model/pothole_model.py +33 -56
- requirements.txt +1 -2
keras_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c72459c9ab862eb75b5d073e54b34dd04e81c1fc117aa64dcff248c97e97840
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size 2453432
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labels.txt
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0 pothole
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1 notpothole
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model/pothole_model.py
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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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# 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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#
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# # pil_image.save(output, format='JPEG')
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# # image_bytes = output.getvalue()
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#
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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 transformers import AutoImageProcessor, AutoModelForImageClassification
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#
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#
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image = Image.open(BytesIO(image_url))
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inputs = processor(images=image, return_tensors="pt")
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#
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logits = outputs.logits
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probabilities = logits.softmax(dim=1)
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# Get predicted class (0: No pothole, 1: Pothole)
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predicted_class = probabilities.argmax().item()
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confidence = probabilities[0, predicted_class].item()
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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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np.set_printoptions(suppress=True)
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# Load the model
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model = load_model("keras_Model.h5", compile=False)
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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).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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requirements.txt
CHANGED
@@ -7,6 +7,5 @@ python-jose[cryptography]
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python-multipart
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certifi
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firebase-admin
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transformers
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pillow
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python-multipart
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certifi
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firebase-admin
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pillow
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tensorflow==2.13.1
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