Spaces:
Runtime error
Runtime error
File size: 3,049 Bytes
28f3c74 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import cv2 as cv
import numpy as np
from fastai.vision.all import *
import time
def chop_img(img, step):
allImgs = []
width, height, i, j = 640, 512, 0, 0
for i in range(step):
for j in range(step):
# crop the image into step*rows and step*columns
imgCrop = img[int(0 + height / step * i): int(height / step + height / step * i),
int(0 + width / step * j): int(width / step + width / step * j)]
imgGray = cv.cvtColor(imgCrop, cv.COLOR_BGR2GRAY)
imgResize = cv.resize(imgGray, (640, 512)) # Resize image
imgAug = augment(imgResize)
allImgs.append((imgResize, imgAug))
j += 1
i += 1
return allImgs
def augment(img):
kernel = np.ones((5, 5), np.uint8)
imgBlur = cv.GaussianBlur(img, (5, 5), 0) # apply gaussian blur
imgThresh = cv.adaptiveThreshold( # apply adaptive threshold
imgBlur, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 7, 5)
imgDilation = cv.dilate(imgThresh, kernel, iterations=1) # apply dilation to amplify threshold result
return imgDilation
def crop_bat(img, box):
x1, y1, x2, y2, x3, y3, x4, y4 = int(box[0][1]), int(box[0][0]), int(box[1][1]), int(box[1][0]), int(box[2][1]), int(box[2][0]), int(box[3][1]), int(box[3][0])
# Find distance from cx to top_left_x and cy to top_left_y to determine how many pixels the border around the cropped image should be
top_left_x, top_left_y, bot_right_x, bot_right_y = min([x1,x2,x3,x4]), min([y1,y2,y3,y4]), max([x1,x2,x3,x4]), max([y1,y2,y3,y4])
crop_x1 = top_left_x - 10
if crop_x1 <= 0:
crop_x1 = 1
crop_x2 = bot_right_x+11
if crop_x2 > 512:
crop_x2 = 512
crop_y1 = top_left_y-10
if crop_y1 <= 0:
crop_y1 = 1
crop_y2 = bot_right_y+11
if crop_y2 > 640:
crop_y2 = 640
bat_crop = img[crop_x1: crop_x2, crop_y1: crop_y2]
return bat_crop
def find_bats(allImgs):
batDepthMin, batDepthMax = 50, 400
cropped_bats = []
for img in allImgs:
blobs = cv.findContours(img[1], cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)[-2]
cropped_bats.extend([crop_bat(img[0], np.int0(cv.boxPoints(cv.minAreaRect(blob)))) for blob in blobs if batDepthMin < cv.contourArea(blob) < batDepthMax])
return cropped_bats
def bat_detector(img):
start=time.time()
allImgs = chop_img(img, 3)
cropped_bats = find_bats(allImgs)
learn = load_learner("model_densenet_zeros.pkl")
def predict(bat):
with learn.no_bar(), learn.no_logging():
start = time.time()
_, _, probs = learn.predict(bat)
print(f"{time.time()-start:.4f}s")
return (tuple(map(lambda x: f"{x:.4f}", probs)))
filtered_results = filter(lambda x: (x[0] < '0.9' and x[1] > '0.5'), [predict(bat) for bat in cropped_bats]) # x[0] is !bat probability and x[1] is bat probability
return len(list(filtered_results)), (time.time()-start) |