def post_process(self, infer_output, image_file):
"""
post_process
"""
print("post process")
data = infer_output[0]
vals = data.flatten()
top_k = vals.argsort()[-1:-6:-1]
print("images:{}".format(image_file))
print("======== top5 inference results: =============")
for n in top_k:
object_class = image_net_classes.get_image_net_class(n)
print("label:%d confidence: %f, class: %s" %
(n, vals[n], object_class))
#using pillow, the category with the highest confidence is written on the image and saved locally
if len(top_k):
object_class = image_net_classes.get_image_net_class(top_k[0])
output_path = os.path.join(os.path.join(SRC_PATH, "../outputs"),
os.path.basename(image_file))
origin_img = Image.open(image_file)
draw = ImageDraw.Draw(origin_img)
font = ImageFont.load_default()
draw.text((10, 50), object_class, font=font, fill=255)
origin_img.save(output_path)
def postprocess(infer_output, image_file):
"""
post_process
"""
print("post process")
data = infer_output[0]
vals = data.flatten()
max_val = np.max(vals)
vals = np.exp(vals - max_val)
sum_val = np.sum(vals)
vals /= sum_val
top_k = vals.argsort()[-1:-6:-1]
print("images:{}".format(image_file))
print("======== top5 inference results: =============")
for n in top_k:
object_class = image_net_classes.get_image_net_class(n)
print("label:%d confidence: %f, class: %s" %
(n, vals[n], object_class))
(filepath, tempfilename) = os.path.split(image_file)
(filename, extension) = os.path.splitext(tempfilename)
output_path = os.path.join(os.path.join(SRC_PATH, "../outputs"),
filename + ".txt")
with open(output_path, "w", encoding="utf-8") as fp:
fp.write(image_net_classes.get_image_net_class(top_k[0]))
def post_process(self, infer_output, image_file):
"""
post_process
"""
print("post process")
data = infer_output[0]
vals = data.flatten()
top_k = vals.argsort()[-1:-7:-1]
print("images:{}".format(image_file))
print("======== top5 inference results: =============")
for n in top_k:
object_class = get_image_net_class(n)
print("label:%d confidence: %f, class: %s" %
(n, vals[n], object_class))
#using pillow, the category with the highest confidence is written on the image and saved locally
if len(top_k):
object_class = get_image_net_class(top_k[0])
object_value = vals[top_k[0]]
output_path = os.path.join(os.path.join(SRC_PATH, "../outputs"),
os.path.basename(image_file))
origin_img = cv2.imread(image_file)
font = cv2.FONT_HERSHEY_SIMPLEX
origin_img = cv2.putText(origin_img, object_class, (10, 100), font,
3, (255, 255, 255), 3)
origin_img = cv2.putText(origin_img, str(object_value), (10, 200),
font, 2, (255, 255, 255), 3)
cv2.imwrite(output_path, origin_img)
def post_process(self, infer_output, batch_image_files, number_of_images):
print("post process")
datas = infer_output[0]
for number in range(number_of_images):
data = datas[number]
vals = data.flatten()
top_k = vals.argsort()[-1:-6:-1]
print("images:{}".format(batch_image_files[number]))
print("======== top5 inference results: =============")
for n in top_k:
object_class = get_image_net_class(n)
print("label:%d confidence: %f, class: %s" %
(n, vals[n], object_class))
#Use Pillow to write the categories with the highest confidence on the image and save them locally
if len(top_k):
object_class = get_image_net_class(top_k[0])
output_path = os.path.join(
"../outputs", os.path.basename(batch_image_files[number]))
origin_img = Image.open(batch_image_files[number])
draw = ImageDraw.Draw(origin_img)
font = ImageFont.load_default()
draw.text((10, 50), object_class, font=font, fill=255)
origin_img.save(output_path)
def post_process(self, infer_output):
print("post process")
data = infer_output[0]
vals = data.flatten()
top_k = vals.argsort()[-1:-6:-1]
print("======== top5 inference results: =============")
for n in top_k:
object_class = get_image_net_class(n)
print("label:%d confidence: %f, class: %s" %
(n, vals[n], object_class))
object_class = get_image_net_class(top_k[0])
return object_class
def post(self):
args = parser_put.parse_args()
img_data_base64_encode = args['img_data']
img_type = args['img_type']
img_decode = base64_decode(img_data_base64_encode, img_type)
#print(img_decode)
result_img_encode = main_process()
os.remove("./origin/origin.jpg")
q = 400
for n in range(1, 1000):
if str(result_img_encode) == get_image_net_class(n):
q = 200
break
result = {"result": str(result_img_encode), "code": q}
return result