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code_image/main.py

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+import os
+import cv2
+import numpy as np
+import argparse
+import sys
+sys.path.append('..')
+from model_processor import ModelProcessor
+import acl
+from acl_resource import AclResource
+
+MODEL_PATH = "../model/body_pose.om"
+DATA_PATH = './tennis_player.jpg'
+
+def execute(model_path, frames_input_src, output_dir):
+    
+
+    ## Initialization ##
+    #initialize acl runtime 
+    acl_resource = AclResource()
+    acl_resource.init()
+    
+    ## Prepare Model ##
+    # parameters for model path and model inputs
+    model_parameters = {
+        'model_dir': model_path,
+        'width': 368, # model input width      
+        'height': 368, # model input height
+    }
+    # perpare model instance: init (loading model from file to memory)
+    # model_processor: preprocessing + model inference + postprocessing
+    model_processor = ModelProcessor(acl_resource, model_parameters)
+
+    ## Get Input ##
+    # Read the image input using OpenCV
+    img_original = cv2.imread(args.frames_input_src)
+
+    ## Model Prediction ##
+    # model_processor.predict: processing + model inference + postprocessing
+    # canvas: the picture overlayed with human body joints and limbs
+    canvas = model_processor.predict(img_original)
+
+    # Save the detected results
+    cv2.imwrite(os.path.join(args.output_dir, 'Result_Pose.jpg'), canvas)
+    
+
+if __name__ == '__main__':   
+    description = 'Load a model for human pose estimation'
+    parser = argparse.ArgumentParser(description=description)
+    parser.add_argument('--model', type=str, default=MODEL_PATH)
+    parser.add_argument('--frames_input_src', type=str,default=DATA_PATH, help="Directory path for image")
+    parser.add_argument('--output_dir', type=str, default='./outputs', help="Output Path")
+
+    args = parser.parse_args()
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+    
+    execute(args.model, args.frames_input_src, args.output_dir)