From 4531fbfe2cb5278b06712f7b5db0b46e389028c4 Mon Sep 17 00:00:00 2001
From: s47700 <s47700@bht-berlin.de>
Date: Tue, 26 Mar 2024 14:15:31 +0100
Subject: [PATCH] Upload New File
---
test/TEST_MODEL_with_VIDEO_1_.py | 149 +++++++++++++++++++++++++++++++
1 file changed, 149 insertions(+)
create mode 100644 test/TEST_MODEL_with_VIDEO_1_.py
diff --git a/test/TEST_MODEL_with_VIDEO_1_.py b/test/TEST_MODEL_with_VIDEO_1_.py
new file mode 100644
index 0000000..23f4049
--- /dev/null
+++ b/test/TEST_MODEL_with_VIDEO_1_.py
@@ -0,0 +1,149 @@
+import torch
+import torchvision
+import torchvision.transforms as transforms
+import torch.nn as nn
+
+
+import os
+import sys
+import numpy as np
+import time
+import yaml
+import cv2
+import matplotlib.pyplot as plt
+
+
+"""
+Author: Jenö Faist, Paul Judis
+"""
+
+"""
+With this File you can use a Video of a 3D Print to Tets your Model.
+By Pressing "f" you mark the current frame as a Fail Print
+else the print is considered good
+"""
+
+"""
+Depending on the Video you must crop it so there a no black lines in it.
+You can find out the cropping by just using try and error.
+(TODO) IMPLEMENT ADAPTIVE CROPING
+"""
+
+def crop_frame(image):
+ cromped_image = image[200:1080-200,620:1920-740,:] #image[:,250:1920-250,:] #image[:,470:1920-700,:] image[:,250:1920-250,:]#image[170:1080-500,650:1920-650,:]
+ return cromped_image
+
+
+if __name__ == '__main__':
+
+ absolutepath = os.path.dirname(__file__)
+ video_PATH = absolutepath+'/TEST_VIDEOS/test_4.mp4'
+ model_save_PATH = absolutepath+'/COMPLETE_MODELS/3D_DEC_MODEL_MIXR18_18E_64B.pt'
+
+
+ print("STARTING CNN VIDEO TEST")
+ print("--------------------")
+ print("Cuda Version: " + torch.version.cuda)
+ print("Cuda: "+str(torch.cuda.is_available()))
+ print("GPU: "+str(torch.cuda.get_device_name()))
+ print("Video PATH: "+video_PATH)
+ print("Model PATH: "+model_save_PATH)
+ print("--------------------")
+ print("Loading Model...")
+
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ """
+ Specifing which Model we using for testing the Data this!
+ THIS MUST BE CHANGED DEPENDING OF WHICH MODEL YOU ARE USING !!!
+ """
+ model = torchvision.models.resnet18(weights='DEFAULT')
+ num_ftrs = model.fc.in_features
+ model.fc = nn.Linear(num_ftrs, 2)
+
+ try:
+ model.load_state_dict(torch.load(model_save_PATH))
+ model.eval()
+ print("[✓] Model Loaded [✓]")
+ except:
+ print("[!?] No Model Found [?!]")
+ exit(1)
+
+ model.eval()
+ model = model.to(device)
+
+ print("Loading VIDEO")
+
+ cap = None
+ try:
+ cap = cv2.VideoCapture(video_PATH)
+ print("[✓] Video Loaded [✓]")
+ except:
+ print("[!?] Video couldn't be found [?!]")
+ exit(1)
+
+ print("--------------------")
+ print("STARTING TEST THE VIDEO 3D PRINT")
+ print("--------------------")
+
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Resize((256,256)),
+ ])
+
+ running_accrucay = 0
+ count = 0
+ VIDEO_ON = True
+ while(VIDEO_ON):
+ count += 1
+ ret, frame = cap.read()
+
+ frame = crop_frame(frame)
+
+ input = transform(frame)
+ img = (input.squeeze()).numpy()
+ img = np.transpose(img, (1, 2, 0))
+
+
+ input = input.to(device)
+ output = model(input.unsqueeze(0))
+ _, preds = torch.max(output, 1)
+
+
+ cv2.imshow('TEST MODEL PRESS "F" TO MARK AS FAIL',img)
+ k =cv2.waitKey(20)
+ FAILED_PRINT = False
+
+ if(k==ord("f")):
+ FAILED_PRINT = True
+
+ if(preds[0].item() == 1 and FAILED_PRINT):
+ running_accrucay +=1
+ elif(preds[0].item() == 0 and not FAILED_PRINT):
+ running_accrucay +=1
+
+ """
+ JUST FOR VISUALS
+ """
+
+ sym = "NO"
+ sym_2 = "NO"
+ if(preds[0].item() == 1):
+ sym = "YES"
+ if FAILED_PRINT:
+ sym_2 = "YES"
+
+
+ sys.stdout.write("\033[K")
+ print("CNN FAIL DETECTED: " + sym + " USER FAIL DETECTED: " + sym_2 + " CURRENT ACCURACY: "+str(int(100*(running_accrucay/count)))+"%", end='\r')
+
+ if k == ord('q'):
+ VIDEO_ON = False
+
+
+ sys.stdout.write("\033[K")
+ print("TESTING VIDEO FINISHED ACCURACY: "+str(int(100*(running_accrucay/count)))+"%", end='\r')
+ cap.release()
+ cv2.destroyAllWindows()
+
--
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