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deploytestertemp.py
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deploytestertemp.py
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#!/usr/bin/env python
# Libraries for deploying CNN
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
import glob
# working VM library
from VMheader import VectorFormatter, BinCompletion
def aflattenerthingy(px, py):
x = [px, py]
x = flatten(x)
x = np.array(x)
x = x.reshape((int(len(x) / 2), 2), order='F')
return x
def acrop_2_sat_pos(positions, path_image, shift):
path_image = path_image.split('/')
path_image = path_image[-1].split('_')
y_change = int(path_image[2])
x_change = int(path_image[-1].split('.')[0])
positions = positions + [x_change * shift, y_change * shift]
return positions
def deg_to_dms(deg): # degrees, minutes, seconds. Returns string
d = int(deg)
md = abs(deg - d) * 60
m = float(md)
return str(d) + str(m)
def flatten(l):
return [item for sublist in l for item in sublist]
def hamming2(s1, s2):
"""Calculate the Hamming distance between two bit strings"""
# assert len(s1) == len(s2)
return sum(c1 != c2 for c1, c2 in zip(s1, s2))
def pred_2_list(prediction):
myorder = [5, 0, 1, 2, 3]
pred = prediction.tolist()
idx = 0
for vector in pred:
pred[idx] = [vector[i] for i in myorder]
idx += 1
# print(pred)
return pred
def main():
# Paths for satellite weights and images
weights = './satwts/yolov712/weights/best.pt'
source = './satellite/cropped_300_scres/'
# Storing values across all reviewed images (probably can go) -IP
# These are used to perform the binning process across the whole thing
vector_sat_total = []
pos_in_sat = []
cropped_true_match = []
cropped_imdist = []
cropped_imangle = []
cropped_feature_class = []
cropped_feature_closest = []
# Initialize
set_logging()
device = select_device(device='0')
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = 640
imgsz = check_img_size(imgsz, s=stride) # check img_size
if half:
model.half() # to FP16
# Set Dataloader
vid_path, vid_writer = None, None
webcam = False
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
old_img_w = old_img_h = imgsz
# Generating dictionary of satellite image crops so that each classes information is saved for each image
satellite_obj = {}
for path, img, im0s, vid_cap in dataset:
# print(path)
sat_name = 'sat_{}'.format(path)
satellite_obj[sat_name] = VectorFormatter('satellite')
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
pred = model(img, augment=True)[0]
# Apply NMS, selecting only the classes that we want to use.
pred = non_max_suppression(pred, .5, .45, classes=(0, 1, 2, 3), agnostic='store_true')
# print(pred.size)
pred = pred[0]
pred = pred.detach().cpu().numpy()
pred = pred_2_list(pred)
satellite_obj[sat_name].cnn_initialization(pred, [old_img_h, old_img_w])
neighbor = 5
if len(satellite_obj[sat_name].p_x) > neighbor:
satellite_obj[sat_name].k_d_tree_test(neighbor)
cropped_imdist.append(satellite_obj[sat_name].im_dist)
cropped_imangle.append(satellite_obj[sat_name].im_angle)
cropped_feature_closest.append(satellite_obj[sat_name].feature_closest)
cropped_feature_class.append(satellite_obj[sat_name].feature_class)
pos_temp = aflattenerthingy(satellite_obj[sat_name].p_x, satellite_obj[sat_name].p_y)
pos_in_sat.append(acrop_2_sat_pos(pos_temp, path, shift=100))
# Performing binning process. Creates histogram of distribution for max vector definition effectiveness and stuff
binn = BinCompletion('binn')
binn.bin_initialize(flatten(cropped_imdist), flatten(cropped_imangle), .8, .8)
for i in range(len(cropped_feature_class)):
vector_sat_all = []
for j in range(len(cropped_feature_class[i])):
vector_sat_all.append(
binn.vector_def(j, cropped_feature_class[i], cropped_imdist[i], cropped_imangle[i],
cropped_feature_closest[i])[0])
vector_sat_total.append(vector_sat_all)
vector_sat_total = flatten(vector_sat_total)
pos_in_sat = np.array(pos_in_sat, dtype=object)
pos_in_sat = flatten(pos_in_sat)
pos_in_sat = np.array(pos_in_sat, dtype=object)
print(pos_in_sat)
# print(vector_sat_total)
# start Drone Nural network
# Paths for satellite weights and images
weights = './satwts/yolov712/weights/best.pt'
source = './satellite/cropped_300_scres/'
# Initialize
set_logging()
device = select_device(device='0')
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = 640
imgsz = check_img_size(imgsz, s=stride) # check img_size
if half:
model.half() # to FP16
# Set Dataloader
vid_path, vid_writer = None, None
webcam = False
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
old_img_w = old_img_h = imgsz
hammingtotal = []
for path, img, im0s, vid_cap in dataset:
drone = VectorFormatter('drone')
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
pred = model(img, augment=True)[0]
# Apply NMS
pred = non_max_suppression(pred, .5, .45, agnostic='store_true')
# print(pred.size)
pred = pred[0]
pred = pred.detach().cpu().numpy()
pred = pred_2_list(pred)
drone.cnn_initialization(pred, [old_img_h, old_img_w])
vector_drone = []
# Checks if enough features are even detected to create a correspondence, the plus 1 is for the humvee
if len(drone.p_x) > neighbor + 1:
# if drone.humvee_detected: # If humvee is detected move on, otherwise go back
drone.k_d_tree_test(neighbor)
for i in range(len(drone.feature_class)):
vector_drone.append(
binn.vector_def(i, drone.feature_class, drone.im_dist, drone.im_angle,
drone.feature_closest)[0])
threshold = 2
matched_idx = np.zeros(shape=(len(vector_drone), 2)) # stored indexes [[idx sat, idx drone],.....,]
for i in range(len(vector_drone)):
best_rating = threshold
for name in satellite_obj:
for k in satellite_obj[name].feature_vectors:
# if vector_drone[i][0:12] == vector_sat_total[j][0:12]:
# compair drone to sat, keep only features of the same classes
rating = hamming2(vector_drone[i], vector_sat_total[j])
if rating <= best_rating:
# print(rating,vector_drone[i],vector_sat_total[j])
best_rating = rating
matched_idx[i] = [i, j]
print(matched_idx[i])
# print(matched_idx)
if __name__ == "__main__":
with torch.no_grad():
main()