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df71107 | 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 | !pip install -q transformers accelerate torch torchvision pillow
import torch
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = Blip2Processor.from_pretrained(
"Salesforce/blip2-flan-t5-xl"
)
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xl",
torch_dtype=torch.float16,
device_map="auto"
)
from google.colab import drive
drive.mount('/content/drive')
prompt = """
You are an automotive damage assessment assistant.
You are given:
• A cropped image of a vehicle damage
• The detected damage type from an object detection model
• The damage area ratio relative to the full image
Your task:
1. Assess the severity of the damage.
2. Decide the required repair action.
Severity must be one of:
- Minor
- Moderate
- Severe
Repair action must be one of:
Dent:
- Dent pull only
- Dent pull + repaint
- Panel repair or replacement
Scratch:
- Polish only
- Repaint
- Fill and repaint
Damage type: dent
Area ratio: 0.037
Respond ONLY in JSON format:
{
"severity": "<Minor | Moderate | Severe>",
"repair_action": "<one valid action>",
"confidence": "<0.0 – 1.0>"
}
"""
inputs = processor(
images=image,
text=prompt,
return_tensors="pt"
).to(device, torch.float16)
generated_ids = model.generate(
**inputs,
max_new_tokens=200
)
output = processor.decode(
generated_ids[0],
skip_special_tokens=True
)
print(output)
import cv2
import numpy as np
from google.colab import files
from matplotlib import pyplot as plt
# upload image
uploaded = files.upload()
img_path = list(uploaded.keys())[0]
# read image
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# blur to remove noise
blur = cv2.GaussianBlur(gray, (5,5), 0)
# detect edges
edges = cv2.Canny(blur, 50, 150)
plt.imshow(edges, cmap='gray')
plt.title("Detected Edges")
plt.show()
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# choose largest contour (assume dent)
largest = max(contours, key=cv2.contourArea)
x,y,w,h = cv2.boundingRect(largest)
dent = img[y:y+h, x:x+w]
plt.imshow(cv2.cvtColor(dent, cv2.COLOR_BGR2RGB))
plt.title("Detected Dent Area")
plt.show()
dent_area = w * h
print("Dent area:", dent_area)
if dent_area < 5000:
dent_type = "Small Dent"
repair = "Paintless Dent Repair"
cost = 5000
elif dent_area < 15000:
dent_type = "Medium Dent"
repair = "Panel Beating"
cost = 12000
else:
dent_type = "Severe Dent"
repair = "Panel Replacement + Paint"
cost = 25000
print("Dent Type:", dent_type)
print("Repair Needed:", repair)
print("Estimated Cost:", cost, "LKR")
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