Create app.py

app.py

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+import gradio as gr
+import cv2
+import joblib
+from skimage.feature import hog
+import numpy as np
+
+MODEL_PATH = 'models/hog_lreg_model_4.pkl'
+clf = joblib.load(MODEL_PATH)
+
+def ocr(pil_image):
+    # Convert PIL image to OpenCV format
+    im = np.array(pil_image)
+    im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
+
+    im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
+    ret, im_th = cv2.threshold(im_gray, 120, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    ctrs, hier = cv2.findContours(im_th, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    bboxes = [cv2.boundingRect(c) for c in ctrs]
+    sorted_bboxes = sorted(bboxes, key=lambda b: b[0])  # Sort by x-coordinate
+
+    plate_char = []
+    image_height, image_width = im.shape[:2]
+    height_threshold = image_height * 0.3
+    width_threshold = image_width * 0.3
+
+    for num, i_bboxes in enumerate(sorted_bboxes):
+        [x, y, w, h] = i_bboxes
+        if h > height_threshold and w < width_threshold:
+            roi = im_gray[y:y + h, x:x + w]
+            roi = cv2.resize(roi, (64, 128), interpolation=cv2.INTER_AREA)
+            roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(1, 1))
+            nbr = clf.predict(np.array([roi_hog_fd]))
+            plate_char.append(str(nbr[0]))
+
+    return ''.join(plate_char)
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=ocr,
+    inputs=gr.Image(type="pil", label="Upload License Plate Image"),
+    outputs=gr.Textbox(label="Predicted License Plate"),
+    title="Automatic License Plate Recognition",
+    description="Upload an image of a license plate, and the system will predict the text on it.",
+)
+
+# Launch the Gradio app
+interface.launch()