def detect(self, image):
detections = self.net.detect_on_image(image,
self.target_size,
self.device,
is_pad=False,
keep_thresh=self.conf_thresh)
bboxs = get_detections(detections, self.conf_thresh)
return [0, bboxs, 0]
def plot_risk(df_risk, real_outbreaks, shocks, ax):
# TODO:add date and not only the #month
risk = df_risk['risk']
detections = utils.get_detections(risk, RISK_THRESH)
ax.plot(risk, lw=3)
ax.axhline(RISK_THRESH, c='gray', ls='--')
for i, d in enumerate(detections):
ax.axvline(x=d, lw=2, alpha=0.5, c='y', label="detections" if i == 0 else None)
try:
ax.fill_between(range(d, d+DETECTION_THRESH), risk[d:d+DETECTION_THRESH], alpha=0.5, color='y')
except:
ax.fill_between(range(d, len(risk)), risk[d:len(risk)], alpha=0.5, color='y')
for i, r in enumerate(real_outbreaks):
ax.axvline(x=r, c='r', label="outbreaks" if i == 0 else None)
for i, s in enumerate(shocks):
ax.axvline(x=s, c='g', label="shocks" if i == 0 else None)
ax.set_ylabel('risk')
ax.set_xlabel('month')
ax.set_ylim(0, 1)
handles, _ = ax.get_legend_handles_labels()
patch = mpatches.Patch(color='y', alpha=0.5, label='Detection window')
handles.append(patch)
ax.legend(handles=handles, loc='upper center')
target_size = (800, 800)
net = SSD("test")
net.load_state_dict(torch.load('weights/WIDERFace_DSFD_RES152.pth'))
net.to(device).eval()
img_path = '/home/fer/Videos/YDXJ2900.jpg'
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
t1 = time.time()
detections = net.detect_on_image(img,
target_size,
device,
is_pad=False,
keep_thresh=conf_thresh)
t2 = time.time()
print("time: {:.2f}".format(t2 - t1))
t1 = time.time()
detections = net.detect_on_image(img,
target_size,
device,
is_pad=False,
keep_thresh=conf_thresh)
t2 = time.time()
print("time: {:.2f}".format(t2 - t1))
vis_detections(img, detections, conf_thresh, show_text=False)
bboxs = get_detections(detections, conf_thresh)
print(bboxs)
def index():
print('Request-form', list(request.form.keys()), file=sys.stderr)
print('Request-form-name', request.form['name'], file=sys.stderr)
# print('Request-form-image',request.form['image'],file=sys.stderr)
image_name = request.form['name']
image_string = request.form['image']
#image_bytes = bytes(image_string,'utf-8')
#image_decoded = base64.decodestring(image_string)
image = Image.open(BytesIO(base64.b64decode(image_string)))
# rotated_image = image.rotate(270,expand=True)
# input_array = np.array(rotated_image)
input_array = np.array(image)
input_array = np.expand_dims(input_array, axis=0)
#result_array = detect.run(input_array)
(_boxes, _scores, _classes,
_masks) = sess.run([boxes, scores, classes, masks],
feed_dict={input_: input_array})
_boxes = np.squeeze(_boxes, axis=0)
_scores = np.squeeze(_scores, axis=0)
_classes = np.squeeze(_classes, axis=0)
_masks = np.squeeze(_masks, axis=0)
input_array = np.squeeze(input_array, axis=0)
detections = utils.get_detections(_scores, config.threshold_score)
utils.draw_bounding_box(input_array, detections, _boxes, _classes,
class_map, _masks)
result_image = Image.fromarray(input_array)
#print('rotated_image.shape = ',input_array.shape)
result_image.save('output.jpg', format='JPEG')
#convert image back to string..
buffered = BytesIO()
result_image.save(buffered, format="JPEG")
final_img_str = base64.b64encode(buffered.getvalue())
# print('Request-files:',request.files,file=sys.stderr)
# print('Requestfiletype:',type(request.files),file=sys.stderr)
# data = request.files.to_dict()
# print('data',data,file=sys.stderr)
# #to-do Input file validation... (ensure input file is valid jpg or png)
# file = data['upload']
# print('File name:',file.filename,file=sys.stderr)
# file_path = os.path.join("Images",file.filename)
# file.save(file_path)
# print('File saved with name:',file.filename,file=sys.stderr)
#Deserialize the image..
# with open(image_name,'wb') as image_file:
# image_file.write(image)
response = final_img_str
# print("Returning Image Response...",file=sys.stderr)
return response
def index():
print('Request-form', list(request.form.keys()), file=sys.stderr)
#print('Request-form-name',request.form['name'],file=sys.stderr)
# print('Request-form-image',request.form['image'],file=sys.stderr)
#image_name = request.form['name']
image_string = request.form['image']
#image_bytes = bytes(image_string,'utf-8')
#image_decoded = base64.decodestring(image_string)
image = Image.open(BytesIO(base64.b64decode(image_string)))
# rotated_image = image.rotate(270,expand=True)
# input_array = np.array(rotated_image)
input_array = np.array(image)
input_array = np.expand_dims(input_array, axis=0)
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(config.mask_model_infer_path,
mode='rb') as graph_file:
serialized_graph = graph_file.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def)
class_map = utils.get_class_map(config.class_map_file)
with tf.Session(graph=detection_graph) as sess:
input_ = sess.graph.get_tensor_by_name("import/image_tensor:0")
boxes = sess.graph.get_tensor_by_name("import/detection_boxes:0")
scores = sess.graph.get_tensor_by_name("import/detection_scores:0")
classes = sess.graph.get_tensor_by_name("import/detection_classes:0")
masks = sess.graph.get_tensor_by_name("import/detection_masks:0")
#result_array = detect.run(input_array)
(_boxes, _scores, _classes,
_masks) = sess.run([boxes, scores, classes, masks],
feed_dict={input_: input_array})
_boxes = np.squeeze(_boxes, axis=0)
_scores = np.squeeze(_scores, axis=0)
_classes = np.squeeze(_classes, axis=0)
_masks = np.squeeze(_masks, axis=0)
input_array = np.squeeze(input_array, axis=0)
detections = utils.get_detections(_scores, config.threshold_score)
utils.draw_bounding_box(input_array, detections, _boxes, _classes,
class_map, _masks)
result_image = Image.fromarray(input_array)
#print('rotated_image.shape = ',input_array.shape)
#result_image.save('output.jpg',format='JPEG')
#convert image back to string..
buffered = BytesIO()
result_image.save(buffered, format="JPEG")
final_img_str = base64.b64encode(buffered.getvalue())
# print('Request-files:',request.files,file=sys.stderr)
# print('Requestfiletype:',type(request.files),file=sys.stderr)
# data = request.files.to_dict()
# print('data',data,file=sys.stderr)
# #to-do Input file validation... (ensure input file is valid jpg or png)
# file = data['upload']
# print('File name:',file.filename,file=sys.stderr)
# file_path = os.path.join("Images",file.filename)
# file.save(file_path)
# print('File saved with name:',file.filename,file=sys.stderr)
#Deserialize the image..
# with open(image_name,'wb') as image_file:
# image_file.write(image)
response = final_img_str
# print("Returning Image Response...",file=sys.stderr)
# tf.reset_defualt_graph();
return response