v0.4.0

Signed-off-by: QAIHM Team <[email protected]>

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
+*.jar filter=lfs diff=lfs merge=lfs -text

apps/android/ImageClassification/README.md

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+### Requirements
+
+1. Java, android-sdk and sdkmanager is already set at user's end
+2. User should have Linux QNN SDK in local machine.
+
+
+## Info
+Right now we use mobilenet_v3_small.tflite model which takes 224x224 as input and gives array of 1000 as output. You can replace it with any tflite classification model, but you have to change the pre-processing, post-processing and dimensions in the app code based on model parameters.
+
+
+## Preprocessing
+
+
+```
+    for (int x = 0; x < input_dims1; x++) {
+        for (int y = 0; y < input_dims2; y++) {
+            int pixel = inputBitmap.getPixel(x, y);
+            List<Float> rgb = Arrays.asList((float)Color.red(pixel), (float)Color.green(pixel), (float)Color.blue(pixel));
+            for(int z = 0;z<3; z++){
+                floatinputarray[0][z][x][y] = (float)((rgb.get(z))-ImageMean.get(z))/ImageStd.get(z);
+            }
+        }
+    }
+```
+
+
+## PostProcessing
+
+
+```
+    public static List<Integer> findTop3Indices(float[] arr) {
+    List<Integer> topIndices = new ArrayList<>();
+
+    for (int i = 0; i < 3; i++) {
+        int maxIndex = 0;
+        float maxValue = arr[0];
+
+        for (int j = 1; j < arr.length; j++) {
+            if (arr[j] > maxValue && !topIndices.contains(j)) {
+                maxValue = arr[j];
+                maxIndex = j;
+            }
+        }
+
+        topIndices.add(maxIndex);
+    }
+
+    return topIndices;
+    }
+```
+
+### Build App:
+
+You have to run build_apk.py for Image Classification. It will generate classification-debug.apk and install it in connected device.
+
+
+    build_apk.py [-h] -q QNNSDK (-m MODEL_PATH | -e MODEL_NAME)
+
+
+
+### Example
+
+Here, with -m, give your tflite model path i.e. till `*.tflite file`, and it will copy model file to assets folder to build andoid app.
+```
+    python build_apk.py -q "<QNN_SDK_PATH>" -m "Path\to\TFLITE\Model"
+```
+
+Also, you can use AI-HUB Model name as mentioned in models directory, to directly export the model from AI-Hub and copy it to app assets.
+
+```
+    python build_apk.py -q "<QNN_SDK_PATH>" -e <Model Name>
+```

apps/android/ImageClassification/build.gradle

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+
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+plugins {
+    id 'com.android.application' version '7.2.1' apply false
+    id 'com.android.library' version '7.2.1' apply false
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

apps/android/ImageClassification/build_apk.py

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+# ---------------------------------------------------------------------
+# Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# ---------------------------------------------------------------------
+import argparse
+import glob
+import os
+import shutil
+import subprocess
+import sys
+from enum import Enum
+
+
+class MODELNAME(Enum):
+    mobilenet_v3_large = 1
+    resnet50 = 2
+    resnext50 = 3
+    inception_v3 = 4
+
+
+def printmenu():
+    print("*****************************")
+    print("*       TYPE OF MODEL       *")
+    print("*****************************")
+    for m in MODELNAME:
+        print(str(m.value) + ". " + m.name)
+    print("*****************************")
+
+
+## Initialize parser
+parser = argparse.ArgumentParser()
+parser.add_argument("-q", "--qnnsdk", required=True, help="Give path of QNN SDK")
+
+parser.add_argument("-m", "--model_name", type=str, help="Model Name")
+
+
+# group = parser.add_mutually_exclusive_group()
+# group.add_argument('-stopdownload', '--stopdownload', action = "store_true", help = "Do NOT Download Model from AI HUB")
+parser.add_argument("-path", "--model_path", type=str, help="TFLITE model file")
+
+args = parser.parse_args()
+
+
+##based on this pre-post can be decided
+if not args.model_name:
+    printmenu()
+    inp_model_name = int(input("Please select one: "))
+    args.model_name = MODELNAME(inp_model_name).name
+
+
+destAsset = os.path.join(".", "classification", "src", "main", "assets")
+if not os.path.exists(destAsset):
+    os.makedirs(destAsset)
+
+
+## MODEL PATH NOT MENTIONED, add information into model_path
+if not args.model_path:
+    exportstatus = input("Do you want us to download the model from AI hub (y/n)")
+
+    ##DOWNLAOD USING EXPORT.PY
+    if exportstatus.lower().startswith("y"):
+        print("EXPORT form path")
+        pathtomodel = os.path.join(
+            "..",
+            "..",
+            "..",
+            "",
+            "qai_hub_models",
+            "models",
+            args.model_name,
+            "export.py",
+        )
+        if not os.path.exists(pathtomodel):
+            print("PATH DO NOT EXIST: " + pathtomodel)
+            exit()
+        subprocess.run(["python", pathtomodel, "--skip-inferencing"])
+        tflite_file = glob.glob(
+            "build" + os.sep + args.model_name + os.sep + "*.tflite", recursive=True
+        )
+        args.model_path = tflite_file[0]
+        # shutil.copy(tflite_file[0], destAsset+os.sep+"superresmodel.tflite")
+
+    ##GET USER TO GIVE PATH
+    else:
+        args.model_path = input("Give model File as input")
+        # if not os.path.exists(tflite_file):
+        # print("PATH DO NOT EXIST: "+tflite_file)
+        # exit()
+        # shutil.copy(tflite_file, destAsset+os.sep+"superresmodel.tflite")
+
+
+if args.model_path:
+    print(args.model_path)
+    if not os.path.exists(args.model_path):
+        print("PATH DO NOT EXIST: " + args.model_path)
+        exit()
+    shutil.copy(args.model_path, destAsset + os.sep + "classification.tflite")
+
+
+## COPYING REQUIRED FILES FROM QNN SDK
+destJNI = os.path.join(".", "classification", "src", "main", "jniLibs", "arm64-v8a")
+if not os.path.exists(destJNI):
+    os.makedirs(destJNI)
+
+# copy *.so from $qnn_sdk/libs/aarch64-android to $jni_lib_dir
+qnnbasiclibs = os.path.join(args.qnnsdk, "lib", "aarch64-android")
+shutil.copytree(qnnbasiclibs, destJNI, dirs_exist_ok=True)
+
+# copy $qnn_sdk/lib/hexagon-v**/unsigned/libQnnHtpV**Skel.so to $jni_lib_dir
+skelstubfiles = os.path.join(args.qnnsdk, "lib", "hexagon-v**", "unsigned", "*.so")
+for file in glob.glob(skelstubfiles):
+    shutil.copy(file, destJNI)
+
+# copy qtld-release.aar to $test_app_root/Application/
+destaar = os.path.join(".", "classification", "libs")
+if not os.path.exists(destaar):
+    os.makedirs(destaar)
+aarfile = os.path.join(args.qnnsdk, "lib", "android", "qtld-release.aar")
+shutil.copy(aarfile, destaar)
+
+
+## BUILDING APK
+if sys.platform.startswith("win"):
+    print("Detected platform is windows")
+    gradleoutput = subprocess.run(["gradlew.bat", "assembleDebug"], cwd=".")
+elif sys.platform.startswith("darwin"):
+    print("Detected platform is MAC")
+    gradleoutput = subprocess.run(["./gradlew", "assembleDebug"], cwd=".")
+else:
+    print("Detected platform is Linux")
+    gradleoutput = subprocess.run(["./gradlew", "assembleDebug"], cwd=".")
+
+
+## COPYING APK TO CWD
+ApkPath = os.path.join(
+    os.getcwd(),
+    "classification",
+    "build",
+    "outputs",
+    "apk",
+    "debug",
+    "classification-debug.apk",
+)
+print("APK Is copied at current Working Directory")
+shutil.copy(ApkPath, ".")
+
+
+install_perm = input("Do you want to install this apk in connected device")
+## INSTALLING AND RUNNING APK
+if install_perm.lower().startswith("y"):
+    command_to_install = ["adb", "install", "classification-debug.apk"]
+    subprocess.run(command_to_install, cwd=".")
+    command_to_run = [
+        "adb",
+        "shell",
+        "am",
+        "start",
+        "-a",
+        "com.example.ACTION_NAME",
+        "-n",
+        "com.qcom.imagesuperres/com.qcom.imagesuperres.QNNActivity",
+    ]
+    subprocess.run(command_to_run, cwd=".")