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predict_video.py
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predict_video.py
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'''
[Fixed] Having issue writing to video
update display cfg
'''
import os
import cv2
import sys
import yaml
import time
import argparse
import numpy as np
import matplotlib.pyplot as plt
##################### model stuff #####################
# System libs
import os, csv, torch, numpy, scipy.io, PIL.Image, torchvision.transforms
# Our libs
from mit_semseg.models import ModelBuilder, SegmentationModule
from mit_semseg.utils import colorEncode
# pass in mode config(yaml file)
# return a dict for the file
# return decoder and encoder weights path
def parse_model_config(path):
with open(path) as file:
data = yaml.load(file, Loader=yaml.FullLoader)
encoder_path = None
decoder_path = None
for p in os.listdir(data['DIR']):
if "encoder" in p.lower():
encoder_path = "{}/{}".format(data['DIR'], p)
continue
if "decoder" in p.lower():
decoder_path = "{}/{}".format(data['DIR'], p)
continue
if encoder_path==None or decoder_path==None:
raise("model weights not found")
return data, encoder_path, decoder_path
def visualize_result(img, pred, index=None, show=True):
# filter prediction class if requested
if index is not None:
pred = pred.copy()
pred[pred != index] = -1
print(f'{names[index+1]}:')
# colorize prediction
pred_color = colorEncode(pred, colors).astype(numpy.uint8)
# aggregate images and save
im_vis = numpy.concatenate((img, pred_color), axis=1)
if show==True:
display(PIL.Image.fromarray(im_vis))
else:
return pred_color, im_vis
def process_img(path=None, frame=None):
# Load and normalize one image as a singleton tensor batch
pil_to_tensor = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], # These are RGB mean+std values
std=[0.229, 0.224, 0.225]) # across a large photo dataset.
])
# pil_image = PIL.Image.open('../ADE_val_00001519.jpg').convert('RGB')
if path!=None:
pil_image = PIL.Image.open(path).convert('RGB')
else:
pil_image = PIL.Image.fromarray(frame)
img_original = numpy.array(pil_image)
img_data = pil_to_tensor(pil_image)
singleton_batch = {'img_data': img_data[None].cuda()}
output_size = img_data.shape[1:]
return (img_original, singleton_batch, output_size)
def predict_img(segmentation_module, singleton_batch, output_size):
# Run the segmentation at the highest resolution.
with torch.no_grad():
scores = segmentation_module(singleton_batch, segSize=output_size)
# Get the predicted scores for each pixel
_, pred = torch.max(scores, dim=1)
pred = pred.cpu()[0].numpy()
return pred
def get_color_palette(pred, bar_height):
pred = np.int32(pred)
pixs = pred.size
top_left_y = 0
bottom_right_y = 30
uniques, counts = np.unique(pred, return_counts=True)
# Create a black image
# bar_height = im_vis.shape[0]
img = np.zeros((bar_height,250,3), np.uint8)
for idx in np.argsort(counts)[::-1]:
color_index = uniques[idx]
name = names[color_index + 1]
ratio = counts[idx] / pixs * 100
if ratio > 0.1:
print("{} {}: {:.2f}% {}".format(color_index+1, name, ratio, colors[color_index]))
img = cv2.rectangle(img, (0,top_left_y), (250,bottom_right_y),
(int(colors[color_index][0]),int(colors[color_index][1]),int(colors[color_index][2])), -1)
img = cv2.putText(img, "{}: {:.3f}%".format(name, ratio), (0,top_left_y+20), 5, 1, (255,255,255), 2, cv2.LINE_AA)
top_left_y+=30
bottom_right_y+=30
return img
def transparent_overlays(image, annotation, alpha=0.5):
img1 = image.copy()
img2 = annotation.copy()
# I want to put logo on top-left corner, So I create a ROI
rows,cols,channels = img2.shape
roi = img1[0:rows, 0:cols ]
# Now create a mask of logo and create its inverse mask also
img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
# Now black-out the area of logo in ROI
# img1_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)
# Take only region of logo from logo image.
img2_fg = cv2.bitwise_and(img2,img2,mask = mask)
# Put logo in ROI and modify the main image
# dst = cv2.add(img1_bg, img2_fg)
dst = cv2.addWeighted(image.copy(), 1-alpha, img2_fg, alpha, 0)
img1[0:rows, 0:cols ] = dst
return dst
##################### model #####################
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="PyTorch Semantic Segmentation Predict on image")
parser.add_argument("-s", "--source", default="0", type=str, metavar='', help="video source")
parser.add_argument("-d", "--display", default=1, type=int, metavar='', help="display real time prediction")
parser.add_argument("-dm", "--dmode", default=0, type=int, metavar='', help="display mode")
# 'outpy.avi' OR 'mp4 file'
parser.add_argument("--save", default=None, type=str, metavar='', help="save prediction video to a directory")
parser.add_argument("--fps", default=5, type=int, metavar='', help="fps of the saved prediction video")
parser.add_argument("-a", "--alpha", default=0.6, type=float, metavar='', help="transparent overlay level")
parser.add_argument("-r", "--ratio", default=0.7, type=float, metavar='', help="ratio for downsampling source")
# parser.add_argument("-s", "--save", default="tmp_results/", type=str, metavar='', help="save prediction to")
parser.add_argument("--cfg", default="config/ade20k-resnet50dilated-ppm_deepsup.yaml",
metavar="FILE", help="path to config file", type=str,)
parser.add_argument("--gpu", default=0, type=int, metavar='', help="gpu id for evaluation")
parser.add_argument("opts", help="Modify config options using the command-line",
default=None, nargs=argparse.REMAINDER, metavar='')
args = parser.parse_args()
mode = args.dmode
# load model
colors = scipy.io.loadmat('data/color150.mat')['colors']
names = {}
with open('data/object150_info.csv') as f:
reader = csv.reader(f)
next(reader)
for row in reader:
names[int(row[0])] = row[5].split(";")[0]
# Network Builders
'''
net_encoder = ModelBuilder.build_encoder(
arch='resnet50dilated',
fc_dim=2048,
weights='ckpt/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth')
net_decoder = ModelBuilder.build_decoder(
arch='ppm_deepsup',
fc_dim=2048,
num_class=150,
weights='ckpt/ade20k-resnet50dilated-ppm_deepsup/decoder_epoch_20.pth',
use_softmax=True)
'''
print("parsing {}".format(args.cfg))
model_config, encoder_path, decoder_path = parse_model_config(args.cfg)
net_encoder = ModelBuilder.build_encoder(
arch = model_config["MODEL"]['arch_encoder'],
fc_dim = model_config['MODEL']['fc_dim'],
weights = encoder_path)
net_decoder = ModelBuilder.build_decoder(
arch = model_config["MODEL"]['arch_decoder'],
fc_dim = model_config['MODEL']['fc_dim'],
num_class = model_config['DATASET']['num_class'],
weights = decoder_path,
use_softmax=True)
crit = torch.nn.NLLLoss(ignore_index=-1)
segmentation_module = SegmentationModule(net_encoder, net_decoder, crit)
segmentation_module.eval()
segmentation_module.cuda()
# creating the videocapture object
# and reading from the input file
# Change it to 0 if reading from webcam
'''
if len(sys.argv) > 2:
print("Usage: python3 {} <optional mp4_file>".format(sys.argv[0]))
exit(1)
elif len(sys.argv) == 1:
source = 0
else:
source = sys.argv[1]
'''
try:
if int(args.source)==0:
source = 0
except:
source = args.source
cap = cv2.VideoCapture(source)
if (args.save)!=None:
# frame_width = int(cap.get(3) * args.ratio + 250)
frame_width = int(cap.get(3) * args.ratio)
frame_width += 250
frame_height = int(cap.get(4) * args.ratio)
if args.dmode==1:
frame_width = (frame_width-250)*2 + 250
if args.dmode==2:
frame_height *= 2
print("w: {}\nh: {}\n".format(frame_width, frame_height))
# out = cv2.VideoWriter("{}tmp_out.avi".format(args.save),cv2.VideoWriter_fourcc('M','J','P','G'), 30, (frame_width, frame_height))
out = cv2.VideoWriter("{}".format(args.save), cv2.VideoWriter_fourcc(*'MP4V'), args.fps, (frame_width,frame_height))
# used to record the time when we processed last frame
# used to record the time at which we processed current frame
prev_frame_time = 0
new_frame_time = 0
# Reading the video file until finished
while(cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
# if video finished or no Video Input
if not ret:
break
# Our operations on the frame come here
gray = frame
# resizing the frame size according to our need, (affects FPS)
# gray = cv2.resize(gray, (600, 350))
gray = cv2.resize(gray, (int(gray.shape[1]*args.ratio), int(gray.shape[0]*args.ratio)))
# font which we will be using to display FPS
font = cv2.FONT_HERSHEY_SIMPLEX
# time when we finish processing for this frame
new_frame_time = time.time()
# Calculating the fps
# fps will be number of frame processed in given time frame
# since their will be most of time error of 0.001 second
# we will be subtracting it to get more accurate result
fps = 1/(new_frame_time-prev_frame_time)
prev_frame_time = new_frame_time
# converting the fps into integer
fps = int(fps)
# by using putText function
fps = str(fps)
# predict
(img_original, singleton_batch, output_size) = process_img(frame=gray)
pred = predict_img(segmentation_module, singleton_batch, output_size)
pred_color, im_vis = visualize_result(img_original, pred, show=False)
# transparent_overlays (mode=0)
if mode==0:
im_vis = transparent_overlays(img_original, pred_color, alpha=args.alpha)
# split org | pred
elif mode==1:
im_vis = numpy.concatenate((img_original, pred_color), axis=1)
elif mode==2:
im_vis = numpy.concatenate((pred_color, img_original), axis=0)
color_palette = get_color_palette(pred, im_vis.shape[0])
im_vis = numpy.concatenate((im_vis, color_palette), axis=1)
# puting the FPS count on the frame
cv2.putText(im_vis, fps, (5, 30), font, 1, (100, 255, 0), 3, cv2.LINE_AA)
# displaying the frame with fps
if (args.save)!=None:
out.write(im_vis)
if (args.display)==1:
# print("\nim_vis.shape: {}\n".format(im_vis.shape))
cv2.imshow('frame', im_vis)
# press 'Q' if you want to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
if (args.save)!=None:
out.release()
# Destroy the all windows now
cv2.destroyAllWindows()