def processing(threshold, jsonFile):
coco_demo = COCODemo(cfg,
min_image_size=800,
confidence_threshold=threshold)
imageList = []
for i, imagePath in enumerate(imagePathList):
print("loading:{}".format(i))
imagefilepath = os.path.join(IMAGE_PATH, imagePath)
image = Image.open(imagefilepath).convert("RGB")
image = np.array(image)[:, :, [2, 1, 0]]
imageList.append(image)
# compute predictions
predictions, errorList, successList = coco_demo.run_on_imageList(
imageList, imagePathList, jsonFile)
for i, prediction in enumerate(predictions):
print("writting:{}".format(i))
targetPath = os.path.join(TARGET_PATH, successList[i])
cv2.imwrite(targetPath, prediction)
imagePathList.remove(successList[i])
if successList[i] in errorPathList:
errorImagePath = os.path.join(ERROR_PATH, successList[i])
errorPathList.remove(successList[i])
os.remove(errorImagePath)
for i, name in enumerate(errorList):
print("writting:{}".format(i))
errorImagePath = os.path.join(IMAGE_PATH, name)
errorImage = cv2.imread(errorImagePath)
targetPath = os.path.join(ERROR_PATH, name)
cv2.imwrite(targetPath, errorImage)
imagePathList = os.listdir(IMAGE_PATH)
imageList = []
jsonFile = open(JSON_PATH, 'w')
for i, imagePath in enumerate(imagePathList):
print("loading:{}".format(i))
imagefilepath = os.path.join(IMAGE_PATH, imagePath)
image = Image.open(imagefilepath).convert("RGB")
image = np.array(image)[:, :, [2, 1, 0]]
imageList.append(image)
# compute predictions
start = time.time()
predictions, errorList, successList = coco_demo.run_on_imageList(
imageList, imagePathList, jsonFile)
for i, prediction in enumerate(predictions):
print("writting:{}".format(i))
targetPath = os.path.join(RESULT_PATH, successList[i])
cv2.imwrite(targetPath, prediction)
for i, name in enumerate(errorList):
print("writting:{}".format(i))
errorImagePath = os.path.join(IMAGE_PATH, name)
errorImage = cv2.imread(errorImagePath)
targetPath = os.path.join(ERROR_PATH, name)
cv2.imwrite(targetPath, errorImage)
end = time.time()
print("run time is {}s".format(end - start))
