Questions for consultation about aics_2023_submission HOT 1 CLOSED

G2 = nx.Graph()
print("image shape ",image.shape,"image name ",image_name)
print("class name ",dirs_names[-2]) # catrgory
print("data type ",dirs_names[-3]) # traintes val
print("image name ",dirs_names)
print("image name ",image_name) #use this
dirname = dirs_names[-2]

target = 0 
if dirname == "tumor":
    target = 1

s = time.time()
if len(image.shape) < 3:
    image = np.stack((image,)*3, axis=-1)

if image.shape[2] > 3:
    image = image[:, :, :3]
print(image.shape,"here here ") 
segments,_ = DISF_Superpixels(image, 8000, 50) 
rag = graph.rag_mean_color(image, segments,mode='distance')
out = color.label2rgb(segments, image, kind='avg')
out = segmentation.mark_boundaries(image, segments, (0, 0, 0))
e = time.time()
print(f"segmentation time = {e-s}")
seg_imgs = []
node_class = {}
start_time = time.time()
for (i, segVal) in enumerate(np.unique(segments)):

    mask = np.zeros(image.shape[:2], dtype = "uint8")
    
    mask[segments == segVal] = 255
    segimg = cv2.cvtColor(cv2.bitwise_and(image, image, mask = mask), cv2.COLOR_BGR2RGB)
    segimg = cv2.bitwise_and(image, image, mask = mask)

    result = np.zeros_like(image)
    result[segments == i] = image[segments == i]

    gray = cv2.cvtColor(segimg, cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]
    # Find contour and sort by contour area
    cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    seg = segimg.copy()
    for c in cnts:
        x,y,w,h = cv2.boundingRect(c)
        seg = image[y:y+h, x:x+w]
        break
    seg = cv2.cvtColor(seg, cv2.COLOR_BGR2RGB)
    rag.nodes[segVal]['image'] = seg
    seg_imgs.append([seg,segVal])


with concurrent.futures.ThreadPoolExecutor(max_workers=30) as executor:
    futures = [executor.submit(fet_from_img, seg_img[0], seg_img[1])  for  i,seg_img in enumerate(seg_imgs)]
    for future in concurrent.futures.as_completed(futures):
        try:
            img_fet,i = future.result()
            print("this result here",i)
            G2.add_node(i, x = img_fet)
        except Exception as exc:
            print(f'generated an exception: {exc} for seg {i}')
            print(traceback.format_exc())
            continue

end_time = time.time()
print(f"{image_name} total time take per image is {end_time - start_time}")
edges = rag.edges
for e in edges:
    G2.add_weighted_edges_from([(e[0],e[1],rag[e[0]][e[1]]['weight'])])

return G2,segments`