old classifier works
This commit is contained in:
parent
03d26b46ca
commit
c79cc2a62c
|
|
@ -5,57 +5,102 @@
|
|||
import numpy as np
|
||||
import tensorflow as tf
|
||||
import cv2
|
||||
import os
|
||||
import os
|
||||
import json
|
||||
|
||||
from Application.Classifiers.ClassifierInterface import ClassifierInterface
|
||||
|
||||
|
||||
class Classifier(ClassifierInterface):
|
||||
def __init__(self):
|
||||
self.threshold = .5
|
||||
print("1")
|
||||
self.model_path = os.path.join(os.path.dirname(__file__), "./class1.pb")
|
||||
self.odapi = self.DetectorAPI(path_to_ckpt=self.model_path)
|
||||
self.threshold = 0.9
|
||||
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.classes[element["id"]] = 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):
|
||||
def detect(self, img):
|
||||
# get the results from the net
|
||||
boxes, scores, classes, num = self.odapi.process_frame(img)
|
||||
res = False
|
||||
for i in range(len(boxes)):
|
||||
if scores[i] > self.threshold:
|
||||
if classes[i] in self.classes:
|
||||
#print(self.classes[classes[i]])
|
||||
return self.classes[classes[i]]
|
||||
|
||||
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
|
||||
|
||||
def tagLayer(self, data):
|
||||
res = []
|
||||
for cnts in data:
|
||||
for cnt in cnts:
|
||||
if cnt.any():
|
||||
x = self.detect(cnt)
|
||||
if x not in res:
|
||||
res.append(x)
|
||||
if x is not None:
|
||||
print(x)
|
||||
cv2.imshow("changes x", cnt)
|
||||
cv2.waitKey(10) & 0XFF
|
||||
return res
|
||||
|
||||
# Detector API can be changed out given the I/O remains the same
|
||||
# this way you can use a different N-Net if you like to
|
||||
class DetectorAPI:
|
||||
def __init__(self, path_to_ckpt):
|
||||
self.path_to_ckpt = path_to_ckpt
|
||||
gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
if gpus:
|
||||
try:
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
except RuntimeError as e:
|
||||
print(e)
|
||||
self.detection_graph = tf.Graph()
|
||||
with self.detection_graph.as_default():
|
||||
od_graph_def = tf.GraphDef()
|
||||
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
|
||||
serialized_graph = fid.read()
|
||||
od_graph_def.ParseFromString(serialized_graph)
|
||||
tf.import_graph_def(od_graph_def, name='')
|
||||
|
||||
self.default_graph = self.detection_graph.as_default()
|
||||
self.sess = tf.Session(graph=self.detection_graph)
|
||||
|
||||
# Definite input and output Tensors for detection_graph
|
||||
self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
|
||||
# Each box represents a part of the image where a particular object was detected.
|
||||
self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
|
||||
# Each score represent how level of confidence for each of the objects.
|
||||
# Score is shown on the result image, together with the class label.
|
||||
self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
|
||||
self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
|
||||
self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
|
||||
|
||||
def process_frame(self, image):
|
||||
# Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
|
||||
image_np_expanded = np.expand_dims(image, axis=0)
|
||||
# Actual detection.
|
||||
|
||||
(boxes, scores, classes, num) = self.sess.run(
|
||||
[self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
|
||||
feed_dict={self.image_tensor: image_np_expanded})
|
||||
|
||||
im_height, im_width,_ = image.shape
|
||||
boxes_list = [None for i in range(boxes.shape[1])]
|
||||
for i in range(boxes.shape[1]):
|
||||
boxes_list[i] = (
|
||||
int(boxes[0, i, 0] * im_height),
|
||||
int(boxes[0, i, 1] * im_width),
|
||||
int(boxes[0, i, 2] * im_height),
|
||||
int(boxes[0, i, 3] * im_width)
|
||||
)
|
||||
|
||||
return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])
|
||||
|
||||
def close(self):
|
||||
self.sess.close()
|
||||
self.default_graph.close()
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
|
||||
|
||||
class ClassifierInterface:
|
||||
def tagLayers(self, layers):
|
||||
def tagLayer(self, layers):
|
||||
"""takes layers, returns list (len(), same as input) of lists with tags for corresponfing layers"""
|
||||
pass
|
||||
|
|
@ -13,7 +13,7 @@ class Config:
|
|||
"maxLength": None,
|
||||
"ttolerance": 60,
|
||||
"videoBufferLength": 16,
|
||||
"noiseThreashold": 0.1,
|
||||
"noiseThreashold": 0.3,
|
||||
"noiseSensitivity": 3/4,
|
||||
"LayersPerContour": 5,
|
||||
"averageFrames": 10
|
||||
|
|
|
|||
|
|
@ -100,7 +100,7 @@ class Layer:
|
|||
|
||||
# the loop isn't nessecary (?) if the number of clusters is known, since it isn't the loop tries to optimize
|
||||
while True:
|
||||
kmeans = KMeans(init="random", n_clusters=clusterCount, n_init=5, max_iter=300, random_state=42)
|
||||
kmeans = KMeans(init="random", n_clusters=clusterCount, n_init=10, max_iter=300, random_state=42)
|
||||
kmeans.fit(mapped)
|
||||
labels = list(kmeans.labels_)
|
||||
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ class LayerManager:
|
|||
def cleanLayers(self):
|
||||
self.freeMin()
|
||||
self.sortLayers()
|
||||
#self.cleanLayers2()
|
||||
self.cleanLayers2()
|
||||
self.freeMax()
|
||||
|
||||
def removeStaticLayers(self):
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ class VideoReader:
|
|||
self.buffer = Queue(config["videoBufferLength"])
|
||||
self.vc = cv2.VideoCapture(videoPath)
|
||||
self.stopped = False
|
||||
self.getWH()
|
||||
if setOfFrames is not None:
|
||||
self.listOfFrames = sorted(setOfFrames)
|
||||
|
||||
|
|
|
|||
6
main.py
6
main.py
|
|
@ -14,8 +14,8 @@ def main():
|
|||
start = time.time()
|
||||
config = Config()
|
||||
|
||||
config["inputPath"] = os.path.join(os.path.dirname(__file__), "generate test footage/out.mp4")
|
||||
#config["importPath"] = os.path.join(os.path.dirname(__file__), "output/short.txt")
|
||||
config["inputPath"] = os.path.join(os.path.dirname(__file__), "generate test footage/3.mp4")
|
||||
config["importPath"] = os.path.join(os.path.dirname(__file__), "output/short.txt")
|
||||
config["outputPath"] = os.path.join(os.path.dirname(__file__), "output/short.mp4")
|
||||
|
||||
vr = VideoReader(config)
|
||||
|
|
@ -34,7 +34,7 @@ def main():
|
|||
layerManager = LayerManager(config, layers)
|
||||
layerManager.cleanLayers()
|
||||
|
||||
#layerManager.tagLayers()
|
||||
layerManager.tagLayers()
|
||||
layers = layerManager.layers
|
||||
exporter = Exporter(config)
|
||||
exporter.export(layers, raw=False)
|
||||
|
|
|
|||
Loading…
Reference in New Issue