update readme ✏️

Author: includeamin

README.md

@@ -16,7 +16,7 @@ after of train you will see result plot:
 - copy TEST forlder from downloaded dataset to dataset directory
 - run this command
 ```bash
-python3 Test/load_test_model.py -d dataset/TEST -m SavedModel/150_epoch_model.hdf5
+python3 test_model.py -d dataset/TEST -m SavedModel/150_epoch_model.hdf5
 ```
 # Todo
 - [ ] Create demo api . [ upload and check the result realtime ]

test_model.py

@@ -0,0 +1,94 @@
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from keras.models import load_model
+from Utils.ImageTools import ImageToArrayPreprocessor
+from PrePorcessor.Preprocessor import SimplePreprocessor
+from dataset.SimpleDatasetLoader import SimpleDatasetLoader
+from keras.optimizers import SGD
+from Model.IncludeNet import IncludeNet
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+from imutils import paths
+import cv2
+
+
+def show(image):
+    # Figure size in inches
+    plt.figure(figsize=(15, 15))
+
+    # Show image, with nearest neighbour interpolation
+    plt.imshow(image, interpolation='nearest')
+    plt.show()
+
+
+def ReadyToUseImage(im):
+    image_blur = cv2.GaussianBlur(im, (7, 7), 0)
+    image_blur_hsv = cv2.cvtColor(image_blur, cv2.COLOR_RGB2HSV)
+    min_red = np.array([80, 60, 140])
+    max_red = np.array([255, 255, 255])
+    image_red1 = cv2.inRange(image_blur_hsv, min_red, max_red)
+    big_contour, mask = find_biggest_contour(image_red1)
+    overlay_mask(mask, im)
+    moments = cv2.moments(mask)
+    centre_of_mass = (
+        int(moments['m10'] / moments['m00']),
+        int(moments['m01'] / moments['m00'])
+    )
+    image_with_com = im.copy()
+    # cv2.rectangle(image_with_com,10,(0,255,0),-1,cv2.LINE_AA)
+    cv2.circle(image_with_com, centre_of_mass, 10, (0, 255, 0), -1, cv2.LINE_AA)
+    # show(image_with_com)
+    image_with_ellipse = im.copy()
+    ellipse = cv2.fitEllipse(big_contour)
+    # print(centre_of_mass)
+    dst = cv2.bitwise_and(im, im, mask=mask)
+
+    return (dst)
+
+
+def find_biggest_contour(image):
+    image = image.copy()
+    s, contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+    biggest_contour = max(contours, key=cv2.contourArea)
+    mask = np.zeros(image.shape, np.uint8)
+    cv2.drawContours(mask, [biggest_contour], -1, 255, -1)
+    return biggest_contour, mask
+
+
+def overlay_mask(mask, image):
+    rgb_mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB)
+    img = cv2.addWeighted(rgb_mask, 0.5, image, 0.5, 0)
+    # show(img)
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-d", "--dataset", required=True, help="halloo insert dataset")
+ap.add_argument("-m", "--model", required=True, help="path to output model")
+args = vars(ap.parse_args())
+size = 50
+classLabels = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"]
+print("[INFO] sampling images ...")
+imagePaths = np.array(list(paths.list_images(args["dataset"])))
+idxs = np.random.randint(0, len(imagePaths), size=(10,))
+imagePaths = imagePaths[idxs]
+sp = SimplePreprocessor(size, size)
+iap = ImageToArrayPreprocessor()
+sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
+(data, labels) = sdl.load(imagePaths)
+data = data.astype("float") / 255.0
+print("[INFO] loading pre-trained network ...")
+model = load_model(args["model"])
+print("[INFO] predicting ...")
+preds = model.predict(data, batch_size=size).argmax(axis=1)
+print(preds)
+for (i, imagePath) in enumerate(imagePaths):
+    # load the example image, draw the prediction, and display it
+    # to our screen
+    image = cv2.imread(imagePath)
+    # image=ReadyToUseImage(image)
+    cv2.putText(image, "Label: {}".format(classLabels[preds[i]]),
+                (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+    cv2.imshow("Image", image)
+    cv2.waitKey(0)