Script for prediction

Author: soham96

trash_classifier.py

@@ -0,0 +1,49 @@
+import keras
+from pypylon import pylon
+from keras.models import Model, load_model
+from keras.applications import mobilenet
+from keras.applications.mobilenet import preprocess_input
+from keras.preprocessing import image
+from keras.utils.generic_utils import CustomObjectScope
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+import cv2
+
+
+def pp_image(img):
+    img = image.load_img('pic.png', target_size=(224, 224))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+
+    return np.asarray(x)
+
+
+model=load_model('models/model1.h5', custom_objects={'relu6': mobilenet.relu6})
+
+camera = pylon.InstantCamera(pylon.TlFactory.GetInstance().CreateFirstDevice())
+
+numberOfImagesToGrab = 100
+camera.StartGrabbingMax(numberOfImagesToGrab)
+
+converter = pylon.ImageFormatConverter()
+converter.OutputPixelFormat = pylon.PixelType_BGR8packed
+converter.OutputBitAlignment = pylon.OutputBitAlignment_MsbAligned
+
+while camera.IsGrabbing():
+    time.sleep(0.005)
+    grabResult = camera.RetrieveResult(5000, pylon.TimeoutHandling_ThrowException)
+
+    if grabResult.GrabSucceeded():
+        # Access the image data.
+        print("SizeX: ", grabResult.Width)
+        print("SizeY: ", grabResult.Height)
+        import ipdb; ipdb.set_trace()
+        img = converter.Convert(grabResult).GetArray()
+        cv2.imwrite('pic.png', img)
+        img=pp_image(img)
+        yo=model.predict(img)
+        print("Gray value of first pixel: ", img[0, 0])
+    
+    grabResult.Release()