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import sys
sys.path.append("/workspaces/tensorflow-gpu/cocoapi/PythonAPI/pycocotools")
import os
import cv2
from detectron2.data import DatasetCatalog
import detectron2.data as ddata
from detectron2.engine import DefaultPredictor
from detectron2.evaluation import COCOEvaluator
from detectron2.utils.visualizer import ColorMode
from detectron2.utils.visualizer import Visualizer
import random
import matplotlib.pyplot as plt
import time
from from_root import from_root
from rcnn_model.utils.floorplan_vectorizer_utils import check_image_size_thresh
results_directory = str(from_root("results"))+"/"
max_image_size = 700*500
def main(cfg,results_filename = "eval_results.txt"):
#update config file
cfg.DATALOADER.NUM_WORKERS = 1
cfg.SOLVER.IMS_PER_BATCH = 1
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
#run evaluation
results = standard_evaluation(cfg)
#save results
file = open(results_directory+results_filename,"w")
file.write(str(results))
file.close()
### Evaluation ###
def standard_evaluation(cfg):
#load predictor
predictor = DefaultPredictor(cfg)
test_data_loader = ddata.build_detection_test_loader(cfg, "inodata_val")
#save some validation images
save_validation_images(predictor)
#create evaluator
evaluator = COCOEvaluator("inodata_val",tasks={"segm","bbox"},output_dir="./eval_output",distributed=False,max_dets_per_image=50,allow_cached_coco=False)
print("EVALUATING")
evaluator.reset()
#load results into evaluator
for inputs, outputs in block_prediction(test_data_loader, predictor):
evaluator.process(inputs,outputs)
del inputs
del outputs
print("|",end="")
time.sleep(.5)
print("")
#run evaluator
results = evaluator.evaluate()
print(results)
print("EVALUATED")
return results
def block_prediction(loader, predictor):
for data in loader:
if(check_image_size_thresh(data[0]["file_name"],max_image_size)):
image = cv2.imread(data[0]["file_name"])
result = predictor(image)
yield data, [result]
del image
del result
### Validation Images ###
def save_validation_images(predictor):
val_img_id = 1
for d in random.sample(DatasetCatalog.get("inodata_val"), 16):
save_image(d,predictor,val_img_id)
val_img_id += 1
def save_image(d, predictor, val_img_id):
try:
if(check_image_size_thresh(d["file_name"],max_image_size)):
#set load image
val_img_dest_path = "models/rcnn/validation_images/RCNN_val_image_"+str(val_img_id)+".png"
im = cv2.imread(d["file_name"])
outputs = predictor(im)
#save image
v = Visualizer(im[:,:,::-1],scale=0.5,instance_mode=ColorMode.IMAGE_BW)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
plt.imshow(out.get_image()[:,:,::-1])
plt.axis('off')
plt.savefig(val_img_dest_path,bbox_inches='tight',pad_inches=0)
print("Saved validation image to "+val_img_dest_path)
plt.clf()
except:
print("ERROR SAVING IMAGE")
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