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karankumbhar47karankumbhar47
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-madding dip project
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‎.gitignore‎

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Python/env/*
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JavaScript/code_with_harry(course)/*
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java/AndroidDevelopment/Android/*
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.idea/
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*.h5

‎dip/demo.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## part 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pixellib\n",
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"from pixellib.semantic import semantic_segmentation\n",
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"from tensorflow.keras.layers import BatchNormalization\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fb79c373060> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
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"1/1 [==============================] - 4s 4s/step\n",
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"Processed Image saved successfully in your current working directory.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"({'class_ids': array([ 0, 15]),\n",
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" 'masks': array([[False, False, False, ..., False, False, False],\n",
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" [False, False, False, ..., False, False, False],\n",
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" [False, False, False, ..., False, False, False],\n",
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" ...,\n",
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" [False, False, False, ..., False, False, False],\n",
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" [False, False, False, ..., False, False, False],\n",
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" [False, False, False, ..., False, False, False]])},\n",
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" array([[[ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" ...,\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0]],\n",
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" \n",
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" [[ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" ...,\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0]],\n",
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" \n",
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" [[ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" ...,\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0],\n",
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" [ 0, 0, 0]],\n",
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" \n",
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" ...,\n",
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" \n",
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" [[ 0, 0, 0],\n",
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" [76, 76, 76],\n",
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" [76, 76, 76],\n",
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" ...,\n",
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" [76, 76, 76],\n",
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" [76, 76, 76],\n",
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" [76, 76, 76]],\n",
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" \n",
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" [[ 0, 0, 0],\n",
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" [76, 76, 76],\n",
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" [76, 76, 76],\n",
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" ...,\n",
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" [76, 76, 76],\n",
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" [76, 76, 76],\n",
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" [76, 76, 76]],\n",
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" \n",
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" [[ 0, 0, 0],\n",
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" [76, 76, 76],\n",
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" [76, 76, 76],\n",
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" ...,\n",
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" [76, 76, 76],\n",
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" [76, 76, 76],\n",
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" [76, 76, 76]]], dtype=uint8))"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"segment_image = semantic_segmentation()\n",
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"segment_image.load_pascalvoc_model(\"deeplabv3_xception_tf_dim_ordering_tf_kernels.h5\")\n",
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"segment_image.segmentAsPascalvoc(\"./images/dark1.jpg\", output_image_name = \"./images/newDark1.jpg\",overlay=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"segment_image = semantic_segmentation()\n",
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"segment_image.load_pascalvoc_model(\"deeplabv3_xception_tf_dim_ordering_tf_kernels.h5\")\n",
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"segment_image.segmentAsPascalvoc(\"./images/dark2.jpg\", output_image_name = \"./images/newDark2.jpg\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"segment_image = semantic_segmentation()\n",
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"segment_image.load_pascalvoc_model(\"deeplabv3_xception_tf_dim_ordering_tf_kernels.h5\")\n",
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"segment_image.segmentAsPascalvoc(\"./images/horse.jpg\", output_image_name = \"./images/newHorse.jpg\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"segment_image = semantic_segmentation()\n",
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"segment_image.load_pascalvoc_model(\"deeplabv3_xception_tf_dim_ordering_tf_kernels.h5\")\n",
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"segment_image.segmentAsPascalvoc(\"./images/cycle.jpg\", output_image_name = \"./images/newCycle.jpg\", overlay = True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## part 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pixellib\n",
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"from pixellib.semantic import semantic_segmentation\n",
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"import cv2\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"segment_image = semantic_segmentation()\n",
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"segment_image.load_pascalvoc_model(\"pascal.h5\")\n",
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"output, segmap = segment_image.segmentAsPascalvoc(\"sample1.jpg\")\n",
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"cv2.imwrite(\".jpg\", output)\n",
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"print(output.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## part 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pixellib\n",
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"from pixellib.semantic import semantic_segmentation\n",
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"import cv2\n",
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"\n",
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"segment_image = semantic_segmentation()\n",
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"segment_image.load_pascalvoc_model(\"pascal.h5\")\n",
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"output, segmap = segment_image.segmentAsPascalvoc(\"./images/horse.png\")\n",
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"cv2.imwrite(\"./images/dog.jpg\", output)\n",
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"print(output.shape)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.0rc1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎dip/images/cycle.jpg‎

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‎dip/images/dark1.jpg‎

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‎dip/images/dark2.jpg‎

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‎dip/images/dog.jpg‎

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‎dip/images/horse.png‎

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‎dip/images/newCycle.jpg‎

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‎dip/images/newDark1.jpg‎

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‎dip/images/newDark2.jpg‎

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