added opencv classifier + docs

Application/Classifiers/ClassifierOCV.py

@@ -0,0 +1,62 @@
+import numpy as np
+import tensorflow as tf
+import cv2
+import os
+import json
+
+from Application.Classifiers.ClassifierInterface import ClassifierInterface
+
+
+class Classifier(ClassifierInterface):
+    def __init__(self):
+        self.threshold = .5
+        with open(os.path.join(os.path.dirname(__file__), "coco_map.json")) as file:
+            mapping = json.load(file)
+            self.classes = dict()
+            for element in mapping:
+                self.classes[element["id"]-1] = element["display_name"]
+
+        self.net = cv2.dnn.readNet(os.path.join(os.path.dirname(
+            __file__), "yolov4.weights"), os.path.join(os.path.dirname(__file__), "yolov4.cfg"))
+        # self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
+        # self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
+        self.layer_names = self.net.getLayerNames()
+        self.outputlayers = [self.layer_names[i[0] - 1]
+                             for i in self.net.getUnconnectedOutLayers()]
+
+        print("Classifier Initiated")
+
+    def tagLayer(self, imgs):
+        # get the results from the net
+        results = []
+        for i, contours in enumerate(imgs[19:20]):
+            # print(i)
+            for contour in contours:
+                height, width, channels = contour.shape
+
+                dim = max(height, width)
+                if dim > 320:
+                    img2 = np.zeros(shape=[dim, dim, 3], dtype=np.uint8)
+                else:
+                    img2 = np.zeros(shape=[320, 320, 3], dtype=np.uint8)
+                img2[:height, :width] = contour
+                blob = cv2.dnn.blobFromImage(
+                    img2, 1/256, (320, 320), (0, 0, 0), True, crop=False)  # reduce 416 to 320
+                self.net.setInput(blob)
+                outs = self.net.forward(self.outputlayers)
+                for out in outs:
+                    for detection in out:
+                        scores = detection
+                        class_id = np.argmax(scores)
+                        confidence = scores[class_id]
+                        if confidence > self.threshold:
+                            if self.classes[class_id] not in results:
+                                cv2.imshow("changes x", img2)
+                                cv2.waitKey(10) & 0XFF
+                                results.append(self.classes[class_id])
+                            #print(self.classes[x], score)
+
+        return results
+
+
+