[UPDATE]add examples

README.md

@@ -42,7 +42,7 @@ index.createIndex()
 # global: Overall similarity using single feature space on the whole image. 
 similar = index.knnQuery('path/to/query/image', k=10, policy='object')
 ```
-
+For detailed usage see [`examples/index.py`](examples/index.py)
 ## Credit
 
 ### YOLOv3: An Incremental Improvement

examples/index.py

@@ -0,0 +1,78 @@
+import cv2
+import glob
+import os
+import sys
+
+import imsearch
+
+
+def create_index(name, images):
+    # Initialize the index
+    index = imsearch.init(name)
+
+    # Clear the index and data if any exists
+    index.cleanIndex()
+
+    # Add single image to index (image path locally stored)
+    index.addImage(images[0])
+
+    # Add image using URL with same interface
+    index.addImage(
+        "https://www.wallpaperup.com/uploads/wallpapers/2014/04/14/332423/d5c09641cb3af3a18087937d55125ae3-700.jpg")
+
+    # Add images in batch (List of image paths locally stored)
+    index.addImageBatch(images[1:])
+
+    return index
+
+
+def create_index_with_config(name):
+    '''
+    parameters:
+        name: name of the index (unique identifier name)
+        MONGO_URI
+        REDIS_URI
+        DETECTOR_MODE:  select 'local' or 'remote'. 
+                        local: detector backend should be running on the same machine
+                        remote: Process should not start detector backend
+        pass any configuration you want to expose as environment variable. 
+    '''
+    index = imsearch.init(name=name,
+                          MONGO_URI='mongodb://localhost:27017/',
+                          REDIS_URI='redis://dummy:[email protected]:6379/0',
+                          DETECTOR_MODE='local')
+
+
+
+def show_results(similar, qImage):
+    qImage = imsearch.utils.load_image(qImage)
+    cv2.imshow('qImage', qImage)
+    for _i, _s in similar:
+        rImage = cv2.imread(_i['image'])
+        print([x['name'] for x in _i['primary']])
+        print(_s)
+        cv2.imshow('rImage', rImage)
+        cv2.waitKey(0)
+
+
+if __name__ == "__main__":
+    all_images = glob.glob(os.path.join(
+        os.path.dirname(__file__), '..', 'images/*.jpg'))
+    index = create_index('test', all_images[:25])
+
+    # query index with image path
+    '''
+    image_path: path to image or URL
+    k: Number of results
+    policy: choose policy from 'object' or 'global'. Search results will change accordingly.
+    '''
+    similar = index.knnQuery(image_path=all_images[25], k=10, policy='object')
+    show_results(similar, all_images[25])
+
+    # query with image URL
+    img_url = 'https://www.wallpaperup.com/uploads/wallpapers/2014/04/14/332423/d5c09641cb3af3a18087937d55125ae3-700.jpg'
+    similar = index.knnQuery(image_path=img_url, k=10, policy='global')
+    show_results(similar, img_url)
+
+    # Create index with configuration
+    index = create_index_with_config('test')

examples/yolo_detector.py

@@ -0,0 +1,44 @@
+from imsearch.backend.object_detector.yolo import Detector
+from imsearch import utils
+from PIL import Image
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from matplotlib.ticker import NullLocator
+
+
+def show_output(output, img):
+    fig, ax = plt.subplots(1)
+    ax.imshow(img)
+
+    for obj in output:
+        box = obj['box']
+        x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
+        box_w = x2 - x1
+        box_h = y2 - y1
+        bbox = patches.Rectangle((x1, y1), box_w, box_h,
+                                 linewidth=2, edgecolor='red', facecolor="none")
+        ax.add_patch(bbox)
+        plt.text(
+            x1,
+            y1,
+            s=obj['name'],
+            color="white",
+            verticalalignment="top",
+            bbox={"color": 'red', "pad": 0},
+        )
+
+    plt.axis("off")
+    plt.gca().xaxis.set_major_locator(NullLocator())
+    plt.gca().yaxis.set_major_locator(NullLocator())
+    plt.show()
+
+
+if __name__ == "__main__":
+    PATH = '../images/000000000139.jpg'
+    detector = Detector()
+
+    img = utils.load_image(PATH)
+    output = detector.predict(img)
+    show_output(output, img)

imsearch/config.py

@@ -3,17 +3,13 @@
 
 default_config = {
     'MONGO_URI': 'mongodb://localhost:27017/',
-    'REDIS_URI': 'redis://localhost:6379/0'
+    'REDIS_URI': 'redis://localhost:6379/0',
+    'DETECTOR_MODE': 'local'
 }
 
 
 def config_init(config):
     final_config = copy.deepcopy(default_config)
     for k, v in config.items():
         final_config[k] = v
-    if final_config['REDIS_URI'] == default_config['REDIS_URI']:
-        final_config['DETECTOR_MODE'] = 'local'
-    else:
-        final_config['DETECTOR_MODE'] = 'remote'
-
     os.environ.update(final_config)

imsearch/index.py

@@ -130,7 +130,7 @@ def addImageBatch(self, image_list):
         for image_path in image_list:
             response.append(self.addImage(image_path))
         return response
-        
+
     def createIndex(self):
         """
         Creates the index. Set create time paramenters and query-time parameters for nmslib index. 
@@ -146,6 +146,10 @@ def knnQuery(self, image_path, k=10, policy='global'):
             The path to the query image.
         k=10
             Number of results.
+        policy='global'
+            choose policy from 'object' or 'global'. Search results will change accordingly.
+            object: Object level matching. The engine will look for similarity at object level for every object detected in the image.
+            global: Overall similarity using single feature space on the whole image.
         """
 
         features = self._feature_extractor.extract(image_path, save=False)