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import gradio as gr
import cv2
import numpy as np
import matplotlib.pyplot as plt
import imutils
import easyocr
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
# Load the model and image processor
processor = AutoImageProcessor.from_pretrained("beingamit99/car_damage_detection")
model = AutoModelForImageClassification.from_pretrained("beingamit99/car_damage_detection")
# Define the function that takes an image as input and returns a text output
def classify_image(input_image):
# Load and process the image
image = np.array(input_image)
inputs = processor(images=image, return_tensors="pt")
# Make predictions
outputs = model(**inputs)
logits = outputs.logits.detach().cpu().numpy()
predicted_class_id = np.argmax(logits)
predicted_proba = np.max(logits)
label_map = model.config.id2label
predicted_class_name = label_map[predicted_class_id]
# OCR
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
bfilter = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(bfilter, 30, 200)
keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(keypoints)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
location = None
for contour in contours:
approx = cv2.approxPolyDP(contour, 10, True)
if len(approx) == 4:
location = approx
break
mask = np.zeros(gray.shape, np.uint8)
new_image = cv2.drawContours(mask, [location], 0, 255, -1)
new_image = cv2.bitwise_and(image, image, mask=mask)
(x, y) = np.where(mask == 255)
(x1, y1) = (np.min(x), np.min(y))
(x2, y2) = (np.max(x), np.max(y))
cropped_image = gray[x1:x2+3, y1:y2+3]
reader = easyocr.Reader(['en'])
result = reader.readtext(cropped_image)
text = result[0][1]
# Return the results
return f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f}", text
# Create Gradio interface
input_image = gr.components.Image()
output_text = gr.components.Text()
output_text2 = gr.components.Text()
gr.Interface(fn=classify_image, inputs=input_image, outputs=[output_text, output_text2], title="AutoVision").launch(debug = 1) |