質問編集履歴

追記

2020/01/07 04:19

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apeirogon0813

スコア117

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@@ -19,3 +19,385 @@
 [こちらの記事は](https://github.com/paramiko/paramiko/issues/1317)英語のため参考になりそうでしたあまり理解できずエラーが治りません．
 ご教示願います．
+コマンドライン上で
+```ここに言語を入力
+ssh a python3.7 test.py
+```
+なお，aは~/.ssh/configに設定されており，ssh-addでパスワードは追加されているものとする
+このaのホスト上でtest.pyを実行し
+/Users/xxx/anaconda3/bin/fab -H edge hello
+を実行し，edgeのホストに接続しようとするとエラーを吐いているようです．
+--test.py--
+```python
+from __future__ import division
+import psutil
+import time
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import numpy as np
+import cv2
+from util import *
+from darknet import Darknet
+from preprocess import prep_image, inp_to_image
+import pandas as pd
+import random
+import argparse
+import pickle as pkl
+import subprocess
+def get_test_input(input_dim, CUDA):
+    img = cv2.imread("imgs/messi.jpg")
+    img = cv2.resize(img, (input_dim, input_dim))
+    img_ =  img[:,:,::-1].transpose((2,0,1))
+    img_ = img_[np.newaxis,:,:,:]/255.0
+    img_ = torch.from_numpy(img_).float()
+    img_ = Variable(img_)
+    if CUDA:
+        img_ = img_.cuda()
+    return img_
+def prep_image(img, inp_dim):
+    """
+    Prepare image for inputting to the neural network.
+    Returns a Variable
+    """
+    orig_im = img
+    dim = orig_im.shape[1], orig_im.shape[0]
+    img = cv2.resize(orig_im, (inp_dim, inp_dim))
+    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
+    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
+    return img_, orig_im, dim
+def write(x, img):
+    c1 = tuple(x[1:3].int())
+    c2 = tuple(x[3:5].int())
+    cls = int(x[-1])
+    print(cls)
+    label = "{0}".format(classes[cls])
+    color = random.choice(colors)
+    cv2.rectangle(img, c1, c2,color, 1)
+    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
+    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
+    cv2.rectangle(img, c1, c2,color, -1)
+    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
+    if label == 'bottle':
+        cmd = "/Users/xxx/anaconda3/bin/fab -H edge hello"
+        subprocess.call(cmd.split())
+    return img
+def arg_parse():
+    """
+    Parse arguements to the detect module
+    """
+    parser = argparse.ArgumentParser(description='YOLO v3 Cam Demo')
+    parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.25)
+    parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
+    parser.add_argument("--reso", dest = 'reso', help =
+                        "Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
+                        default = "160", type = str)
+    return parser.parse_args()
+if __name__ == '__main__':
+    cfgfile = "cfg/yolov3.cfg"
+    weightsfile = "yolov3.weights"
+    num_classes = 80
+    i = 0
+    args = arg_parse()
+    confidence = float(args.confidence)
+    nms_thesh = float(args.nms_thresh)
+    start = 0
+    CUDA = torch.cuda.is_available()
+    num_classes = 80
+    bbox_attrs = 5 + num_classes
+    model = Darknet(cfgfile)
+    model.load_weights(weightsfile)
+    model.net_info["height"] = args.reso
+    inp_dim = int(model.net_info["height"])
+    assert inp_dim % 32 == 0
+    assert inp_dim > 32
+    if CUDA:
+        model.cuda()
+    model.eval()
+    videofile = 'video.avi'
+    cap = cv2.VideoCapture("http://192.168.xxx.xxx:8080/?action=stream")
+    assert cap.isOpened(), 'Cannot capture source'
+    frames = 0
+    start = time.time()
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if ret:
+            img, orig_im, dim = prep_image(frame, inp_dim)
+#            im_dim = torch.FloatTensor(dim).repeat(1,2)
+            if CUDA:
+                im_dim = im_dim.cuda()
+                img = img.cuda()
+            output = model(Variable(img), CUDA)
+            output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
+            if type(output) == int:
+                frames += 1
+                print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+                cv2.imshow("frame", orig_im)
+                key = cv2.waitKey(1)
+                if key & 0xFF == ord('q'):
+                    break
+                continue
+            output[:,1:5] = torch.clamp(output[:,1:5], 0.0, float(inp_dim))/inp_dim
+#            im_dim = im_dim.repeat(output.size(0), 1)
+            output[:,[1,3]] *= frame.shape[1]
+            output[:,[2,4]] *= frame.shape[0]
+            classes = load_classes('data/coco.names')
+            colors = pkl.load(open("pallete", "rb"))
+            list(map(lambda x: write(x, orig_im), output))
+            cv2.imshow("frame", orig_im)
+            key = cv2.waitKey(1)
+            if key & 0xFF == ord('q'):
+                break
+            frames += 1
+            print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+        else:
+            break
+```

追記

2020/01/07 04:18

投稿

apeirogon0813

スコア117

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