11 KiB
11 KiB
import cv2
import matplotlib.pyplot as plt
import keras
import numpy as np
import threading
import tensorflow as tfdef wrap_frozen_graph(graph_def, inputs, outputs, print_graph=False):
def _imports_graph_def():
tf.compat.v1.import_graph_def(graph_def, name="")
wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, [])
import_graph = wrapped_import.graph
if print_graph == True:
print("-" * 50)
print("Frozen model layers: ")
layers = [op.name for op in import_graph.get_operations()]
for layer in layers:
print(layer)
print("-" * 50)
return wrapped_import.prune(
tf.nest.map_structure(import_graph.as_graph_element, inputs),
tf.nest.map_structure(import_graph.as_graph_element, outputs)) # Load frozen graph using TensorFlow 1.x functions
with tf.io.gfile.GFile("./frozen_models/frozen_graph2.pb", "rb") as f:
graph_def = tf.compat.v1.GraphDef()
loaded = graph_def.ParseFromString(f.read())
# Wrap frozen graph to ConcreteFunctions
frozen_func = wrap_frozen_graph(graph_def=graph_def,
inputs=["x:0"],
outputs=["Identity:0"],
print_graph=False)-------------------------------------------------- Frozen model layers: x sequential/conv2d/Conv2D/ReadVariableOp/resource sequential/conv2d/Conv2D/ReadVariableOp sequential/conv2d/Conv2D sequential/conv2d/BiasAdd/ReadVariableOp/resource sequential/conv2d/BiasAdd/ReadVariableOp sequential/conv2d/BiasAdd sequential/conv2d/Relu sequential/batch_normalization/ReadVariableOp/resource sequential/batch_normalization/ReadVariableOp sequential/batch_normalization/ReadVariableOp_1/resource sequential/batch_normalization/ReadVariableOp_1 sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp/resource sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp_1/resource sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp_1 sequential/batch_normalization/FusedBatchNormV3 sequential/max_pooling2d/MaxPool sequential/conv2d_1/Conv2D/ReadVariableOp/resource sequential/conv2d_1/Conv2D/ReadVariableOp sequential/conv2d_1/Conv2D sequential/conv2d_1/BiasAdd/ReadVariableOp/resource sequential/conv2d_1/BiasAdd/ReadVariableOp sequential/conv2d_1/BiasAdd sequential/conv2d_1/Relu sequential/batch_normalization_1/ReadVariableOp/resource sequential/batch_normalization_1/ReadVariableOp sequential/batch_normalization_1/ReadVariableOp_1/resource sequential/batch_normalization_1/ReadVariableOp_1 sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp/resource sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp_1/resource sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp_1 sequential/batch_normalization_1/FusedBatchNormV3 sequential/max_pooling2d_1/MaxPool sequential/conv2d_2/Conv2D/ReadVariableOp/resource sequential/conv2d_2/Conv2D/ReadVariableOp sequential/conv2d_2/Conv2D sequential/conv2d_2/BiasAdd/ReadVariableOp/resource sequential/conv2d_2/BiasAdd/ReadVariableOp sequential/conv2d_2/BiasAdd sequential/conv2d_2/Relu sequential/batch_normalization_2/ReadVariableOp/resource sequential/batch_normalization_2/ReadVariableOp sequential/batch_normalization_2/ReadVariableOp_1/resource sequential/batch_normalization_2/ReadVariableOp_1 sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp/resource sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp_1/resource sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp_1 sequential/batch_normalization_2/FusedBatchNormV3 sequential/conv2d_3/Conv2D/ReadVariableOp/resource sequential/conv2d_3/Conv2D/ReadVariableOp sequential/conv2d_3/Conv2D sequential/conv2d_3/BiasAdd/ReadVariableOp/resource sequential/conv2d_3/BiasAdd/ReadVariableOp sequential/conv2d_3/BiasAdd sequential/conv2d_3/Relu sequential/batch_normalization_3/ReadVariableOp/resource sequential/batch_normalization_3/ReadVariableOp sequential/batch_normalization_3/ReadVariableOp_1/resource sequential/batch_normalization_3/ReadVariableOp_1 sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp/resource sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp_1/resource sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp_1 sequential/batch_normalization_3/FusedBatchNormV3 sequential/conv2d_4/Conv2D/ReadVariableOp/resource sequential/conv2d_4/Conv2D/ReadVariableOp sequential/conv2d_4/Conv2D sequential/conv2d_4/BiasAdd/ReadVariableOp/resource sequential/conv2d_4/BiasAdd/ReadVariableOp sequential/conv2d_4/BiasAdd sequential/conv2d_4/Relu sequential/batch_normalization_4/ReadVariableOp/resource sequential/batch_normalization_4/ReadVariableOp sequential/batch_normalization_4/ReadVariableOp_1/resource sequential/batch_normalization_4/ReadVariableOp_1 sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp/resource sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp_1/resource sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp_1 sequential/batch_normalization_4/FusedBatchNormV3 sequential/max_pooling2d_2/MaxPool sequential/flatten/Const sequential/flatten/Reshape sequential/dense/MatMul/ReadVariableOp/resource sequential/dense/MatMul/ReadVariableOp sequential/dense/MatMul sequential/dense/BiasAdd/ReadVariableOp/resource sequential/dense/BiasAdd/ReadVariableOp sequential/dense/BiasAdd sequential/dense/Relu sequential/dense_1/MatMul/ReadVariableOp/resource sequential/dense_1/MatMul/ReadVariableOp sequential/dense_1/MatMul sequential/dense_1/BiasAdd/ReadVariableOp/resource sequential/dense_1/BiasAdd/ReadVariableOp sequential/dense_1/BiasAdd sequential/dense_1/Relu sequential/dense_2/MatMul/ReadVariableOp/resource sequential/dense_2/MatMul/ReadVariableOp sequential/dense_2/MatMul sequential/dense_2/BiasAdd/ReadVariableOp/resource sequential/dense_2/BiasAdd/ReadVariableOp sequential/dense_2/BiasAdd sequential/dense_2/Softmax NoOp Identity --------------------------------------------------
cap = cv2.VideoCapture("rybki.mp4")
cap.set(cv2.CAP_PROP_FPS, 60)
class_names=sorted(['Fish', "Jellyfish", 'Penguin', 'Puffin', 'Shark', 'Starfish', 'Stingray'])
object_detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter('track_fish.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (width, height))
while True:
ret, frame = cap.read()
if(frame is not None):
roi = frame[100: 900,330:1900]
mask = object_detector.apply(roi)
_, mask = cv2.threshold(mask,254,255, cv2.THRESH_BINARY)
conturs, _ =cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
images = []
for cnt in conturs:
area = cv2.contourArea(cnt)
if area > 200:
#cv2.drawContours(roi,[cnt],-1,(0,255,0),2)
x,y,w,h = cv2.boundingRect(cnt)
rectangle = cv2.rectangle(roi,(x,y),(x+w,y+h),(0,255,0),3)
image_to_predict = roi[y:y+h,x:x+w]
image_to_predict = cv2.resize(image_to_predict,(227,227))
# images.append((x,y,rectangle,np.expand_dims(image_to_predict,axis=0)))
pred = frozen_func(x=tf.convert_to_tensor(image_to_predict[None, :], dtype='float32'))
label = class_names[np.argmax(pred)]
cv2.putText(rectangle, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 1)
# if images:
# pred = model.predict(np.vstack([image[3] for image in images]))
# labels = [class_names[np.argmax(pre)] for pre in pred]
# for i,image in enumerate(images):
roi = cv2.resize(roi, (960, 540))
cv2.imshow("roi", roi)
key = cv2.waitKey(30)
if key == 27:
break
#out.write(frame)
else:
break
#out.release()
cap.release()
cv2.destroyAllWindows()