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code_video/main.py

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+import random
+import os
+import cv2
+import numpy as np
+import argparse
+import sys
+sys.path.append('..')
+from model_processor import ModelProcessor
+from atlas_utils.camera import Camera
+from atlas_utils import presenteragent
+from atlas_utils.acl_image import AclImage
+import acl
+from acl_resource import AclResource
+
+MODEL_PATH = "../model/body_pose.om"
+BODYPOSE_CONF="../body_pose.conf"
+CAMERA_FRAME_WIDTH = 1280
+CAMERA_FRAME_HEIGHT = 720
+DATA_PATH = './yoga.mp4'
+
+
+def execute(model_path, frames_input_src, output_dir, is_presenter_server):
+
+    ## 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 video input using OpenCV
+    cap = cv2.VideoCapture(frames_input_src)
+
+    ## Set Output ##
+    if is_presenter_server:
+        # if using presenter server, then open the presenter channel
+        chan = presenteragent.presenter_channel.open_channel(BODYPOSE_CONF)
+        if chan == None:
+            print("Open presenter channel failed")
+            return
+    else:
+        # if saving result as video file (mp4), then set the output video writer using opencv
+        video_output_path = '{}/demo-{}-{}.mp4'.format(output_dir, os.path.basename(frames_input_src), str(random.randint(1, 100001)))
+        video_writer = cv2.VideoWriter(video_output_path, 0x7634706d, 25,
+                                                (1280, 720))
+        if video_writer == None:
+            print('Error: cannot get video writer from openCV')
+
+
+    while(cap.isOpened()):
+        ## Read one frame of the input video ## 
+        ret, img_original = cap.read()
+
+        if not ret:
+            print('Cannot read more, Reach the end of video')
+            break
+
+        ## 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)
+        
+        ## Present Result ##
+        if is_presenter_server:
+            # convert to jpeg image for presenter server display
+            _,jpeg_image = cv2.imencode('.jpg',canvas)
+            # construct AclImage object for presenter server
+            jpeg_image = AclImage(jpeg_image, img_original.shape[0], img_original.shape[1], jpeg_image.size)
+            # send to presenter server
+            chan.send_detection_data(img_original.shape[0], img_original.shape[1], jpeg_image, [])
+
+        else:
+            # save to video
+            video_writer.write(canvas)
+    
+    # release the resources
+    cap.release()
+    if not is_presenter_server:
+        video_writer.release()
+   
+
+    
+
+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 video.")
+    parser.add_argument('--output_dir', type=str, default='./outputs', help="Output Path")
+    parser.add_argument('--is_presenter_server', type=bool, default=False, help="Display on presenter server or save to a video mp4 file (T/F)")
+    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, args.is_presenter_server)