61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
import json
|
|
import os
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
from Application.Classifiers.ClassifierInterface import ClassifierInterface
|
|
|
|
|
|
class Classifier(ClassifierInterface):
|
|
def __init__(self):
|
|
self.threshold = 0.5
|
|
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
|