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2.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9711334db2b01d5894feb8ed0f5cb3e97d125b8d229f8d8692f625801818f5ef
+size 2780051

app.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+
+def remove_background_deeplab(image_path):
+    # Load the TensorFlow Lite model
+    interpreter = tf.lite.Interpreter(model_path="2.tflite")
+    interpreter.allocate_tensors()
+
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    # Load and preprocess the image
+    image = cv2.imread(image_path)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Resize the image to match the expected input size of the model
+    input_size = (257, 257)
+    image_resized = cv2.resize(image_rgb, input_size)
+
+    # Normalize the input image
+    input_tensor = (image_resized / 127.5 - 1.0).astype(np.float32)
+
+    # Set the input tensor to the model
+    interpreter.set_tensor(input_details[0]['index'], np.expand_dims(input_tensor, axis=0))
+
+    # Run inference
+    interpreter.invoke()
+
+    # Get the segmented mask
+    predictions = interpreter.get_tensor(output_details[0]['index'])
+    mask = np.argmax(predictions, axis=-1)[0]
+
+    # Resize the binary mask to match the shape of the image
+    binary_mask = cv2.resize(np.where(mask == 15, 1, 0).astype(np.uint8), (image.shape[1], image.shape[0]))
+
+    # Multiply the image with the binary mask to get the result
+    result = image * binary_mask[:, :, np.newaxis]
+
+    # Display the result or save it
+    cv2.imshow('Result', result)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+# Example usage
+# remove_background_deeplab('images (2).jpeg')
+gr.Interface(fn=remove_background_deeplab,inputs=gr.Image(label='Drop an Image or Open Camera to Classify'),outputs=gr.Image()).launch()