Update sample code

Author: quic-zhanweiw

samples/python/inception_v3/inception_v3.py

@@ -14,6 +14,7 @@
 import torchvision.transforms as transforms
 from PIL import Image
 from PIL.Image import fromarray as ImageFromArray
+import argparse
 
 from qai_appbuilder import (QNNContext, Runtime, LogLevel, ProfilingLevel, PerfProfile, QNNConfig)
 
@@ -45,6 +46,12 @@
 
 inceptionV3 = None
 
+def format_float(num, max_zeros=6):
+    if abs(num) >= 1e-6:
+        return f"{num:.10f}".rstrip('0').rstrip('.')
+    else:
+        return f"{num:.{max_zeros+2}f}".rstrip('0').rstrip('.')
+
 def preprocess_PIL_image(image: Image) -> torch.Tensor:
     preprocess = transforms.Compose([
         transforms.Resize(IMAGE_SIZE),
@@ -62,9 +69,19 @@ def post_process(probabilities, output):
         categories = [s.strip() for s in f.readlines()]
     # Show top categories per image
     top5_prob, top5_catid = torch.topk(probabilities, 5)
-    print("Top 5 predictions for image:\n")
+
+    result = "Top 5 predictions for image:\n"
+    print(result)
+
     for i in range(top5_prob.size(0)):
-        print(categories[top5_catid[i]], top5_prob[i].item())
+        cat_id = categories[top5_catid[i]]
+        item_value = top5_prob[i].item()
+        item_value = format_float(item_value)
+        result += f'{cat_id} {item_value} \n'
+
+        print(categories[top5_catid[i]], item_value)
+
+    return result
 
 # InceptionV3 class which inherited from the class QNNContext.
 class InceptionV3(QNNContext):
@@ -115,19 +132,33 @@ def Inference(input_image_path):
     # show the Top 5 predictions for image
     output = torch.from_numpy(output_data)  
     probabilities = torch.softmax(output, dim=0)
-    post_process(probabilities, output)
-    
+    result=post_process(probabilities, output)
+
+    return result
 
 def Release():
     global inceptionV3
 
     # Release the resources.
     del(inceptionV3)
 
+def main(input = None):
+
+    if input is None:
+        input = execution_ws + "\\input.jpg"
+
+    Init()
+
+    result = Inference(input)
+
+    Release()
 
-Init()
+    return result
 
-Inference(execution_ws + "\\input.jpg")
 
-Release()
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Process a single image path.")
+    parser.add_argument('--image', help='Path to the image', default=None)
+    args = parser.parse_args()
 
+    main(args.image)

samples/python/yolov8_det/yolov8_det.py

@@ -17,6 +17,8 @@
 from torch.nn.functional import interpolate, pad
 from torchvision.ops import nms
 from typing import List, Tuple, Optional, Union, Callable
+import argparse
+
 from qai_appbuilder import (QNNContext, Runtime, LogLevel, ProfilingLevel, PerfProfile, QNNConfig)
 
 ####################################################################
@@ -292,7 +294,7 @@ def Init():
     # Instance for YoloV8 objects.
     yolov8 = YoloV8("yolov8", madel_path)
 
-def Inference(input_image_path, output_image_path):
+def Inference(input_image_path, output_image_path, show_image = True):
     global image_buffer, nms_iou_threshold, nms_score_threshold
 
     # Read and preprocess the image.
@@ -349,7 +351,9 @@ def Inference(input_image_path, output_image_path):
     #save and display the output_image
     output_image = Image.fromarray(output_image)
     output_image.save(output_image_path)
-    output_image.show()
+
+    if show_image:
+        output_image.show()
 
 def Release():
     global yolov8
@@ -358,9 +362,28 @@ def Release():
     del(yolov8)
 
 
-Init()
+def main(input_image_path=None, output_image_path=None, show_image = True):
+    
+    if input_image_path is None:
+        input_image_path = execution_ws + "\\input.jpg"
+
+    if output_image_path is None:
+        output_image_path = execution_ws + "\\output.png"
+
+    Init()
+
+    Inference(input_image_path=input_image_path,output_image_path=output_image_path,show_image=show_image)
+
+    Release()
+
+    return "Yolo V8 Inference Result"
 
-Inference(execution_ws + "\\input.jpg", execution_ws + "\\output.jpg")
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Process a single image path.")
+    parser.add_argument('--input_image_path', help='Path to the input image', default=None)
+    #input_image_path, output_image_path
+    parser.add_argument('--output_image_path', help='Path to the output image', default=None)
+    args = parser.parse_args()
 
-Release()
+    main(args.input_image_path, args.output_image_path)