def scan_image(path):
# load the image and define the window width and height
image = cv2.imread(path, cv2.IMREAD_COLOR)
# loop over the image pyramid
level = 0
lst_img = list()
lst_imgDetail = list()
ori_clone = image.copy()
overlapImg = image.copy()
for resized_image in pyramid(image, scale):
# loop over the sliding window for each layer of the pyramid
for (x, y, window) in sliding_window(resized_image, stepSize=stepSize, windowSize=(winW, winH)):
# if the window does not meet our desired window size, ignore it
if window.shape[0] != winH or window.shape[1] != winW:
continue
# THIS IS WHERE YOU WOULD PROCESS YOUR WINDOW, SUCH AS APPLYING A
# MACHINE LEARNING CLASSIFIER TO CLASSIFY THE CONTENTS OF THE
# WINDOW
curWindow = (x, y, x + winW, y + winH)
subImage = utils.to_image(resized_image).crop(curWindow)
normalized_img = pre_processing_data.process_single_file(subImage)
lst_img.append(normalized_img)
imgDetail = (x, y, level, resized_image)
lst_imgDetail.append(imgDetail)
level += 1
# Predict all window
lst_indexPositive, positive_scores = predict_multi(lst_img, model_path)
time_now("fusing")
for i in lst_indexPositive:
subX, subY, subLevel, subImg = lst_imgDetail[i]
ori_x, ori_y, new_winW, new_winH = reverse_window(subX, subY, subImg.shape[1], subImg.shape[0],
scale ** subLevel, image.shape[1], image.shape[0], winW, winH)
# Get positive image and save it
ori_window = (ori_x, ori_y, ori_x + new_winW, ori_y + new_winH)
# Draw rectangle on output image
cv2.rectangle(ori_clone, (ori_x, ori_y), (ori_x + new_winW, ori_y + new_winH), (0, 255, 0), 2)
lstRect.append(ori_window)
overlappedLst = run_NMS(lstRect, positive_scores, 0.05)
time_now("draw rect")
for i in overlappedLst:
x1, y1, x2, y2 = lstRect[i]
cv2.rectangle(overlapImg, (x1, y1), (x2, y2), (0, 255, 0), 2)
result_path = os.path.join(stored_path, 'result.png')
not_overlap = os.path.join(stored_path, 'be4.png')
utils.to_image(ori_clone).save(not_overlap, 'png')
utils.to_image(overlapImg).save(result_path, 'png')
return result_path, len(overlappedLst)
# loop over the sliding window for each layer of the pyramid
# for (x, y, window) in sliding_window(resized_image, stepSize=8, windowSize=(winW, winH)):
for (x, y, window) in sliding_window(resized_image,
stepSize=16,
windowSize=(winW, winH)):
# if the window does not meet our desired window size, ignore it
if window.shape[0] != winH or window.shape[1] != winW:
continue
# THIS IS WHERE YOU WOULD PROCESS YOUR WINDOW, SUCH AS APPLYING A
# MACHINE LEARNING CLASSIFIER TO CLASSIFY THE CONTENTS OF THE
# WINDOW
curWindow = (x, y, x + winW, y + winH)
subImage = utils.to_image(resized_image).crop(curWindow)
normalized_img = pre_processing_data.process_single_file(subImage)
lst_img.append(normalized_img)
imgDetail = (x, y, level, resized_image)
lst_imgDetail.append(imgDetail)
# since we do not have a classifier, we'll just draw the window
# clone = resized_image.copy()
# cv2.rectangle(clone, (x, y), (x + winW, y + winH), (0, 255, 0), 2)
# cv2.imshow("Window", clone)
# cv2.waitKey(1)
# time.sleep(0.025)
level += 1
# Predict all window