From 5ba91c38708b5af6e227c0742426b805d39bfafe Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sat, 15 Sep 2018 00:29:08 +0000
Subject: [PATCH] temp
---
opencv_dnn.py | 132 +++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 129 insertions(+), 3 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index d923fc2..25d5848 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -2,6 +2,7 @@
import numpy as np
import os
import sys
+import math
from operator import itemgetter
@@ -68,7 +69,104 @@
cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
+def remove_glare(img):
+ """
+ Inspired from:
+ http://www.amphident.de/en/blog/preprocessing-for-automatic-pattern-identification-in-wildlife-removing-glare.html
+ The idea is to find area that has low saturation but high value, which is what a glare usually look like.
+ """
+ img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+ _, s, v = cv2.split(img_hsv)
+ non_sat = (s < 32) * 255 # Find all pixels that are not very saturated
+
+ # Slightly decrease the area of the non-satuared pixels by a erosion operation.
+ disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+ non_sat = cv2.erode(non_sat.astype(np.uint8), disk)
+
+ # Set all brightness values, where the pixels are still saturated to 0.
+ v[non_sat == 0] = 0
+ # filter out very bright pixels.
+ glare = (v > 240) * 255
+
+ # Slightly increase the area for each pixel
+ glare = cv2.dilate(glare.astype(np.uint8), disk)
+ glare_reduced = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 200
+ glare = cv2.cvtColor(glare, cv2.COLOR_GRAY2BGR)
+ corrected = np.where(glare, glare_reduced, img)
+ return corrected
+
+
+def find_card(img, thresh_val=80, blur_radius=None, dilate_radius=None, min_hyst=80, max_hyst=200, min_line_length=None, max_line_gap=None, debug=False):
+ # Default values
+ if blur_radius is None:
+ blur_radius = math.floor(min(img.shape[:2]) / 100 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
+ if dilate_radius is None:
+ dilate_radius = math.floor(min(img.shape[:2]) / 67 + 0.5)
+ if min_line_length is None:
+ min_line_length = min(img.shape[:2]) / 3
+ if max_line_gap is None:
+ max_line_gap = min(img.shape[:2]) / 10
+
+ thresh_radius = math.floor(min(img.shape[:2]) / 50 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
+
+ print(blur_radius, dilate_radius, thresh_radius, min_line_length, max_line_gap)
+ '''
+ blur_radius = 3
+ dilate_radius = 3
+ thresh_radius = 3
+ min_line_length = 5
+ max_line_gap = 5
+ '''
+
+ img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ # Median blur better removes background textures than Gaussian blur
+ img_blur = cv2.medianBlur(img_gray, blur_radius)
+ # Truncate the bright area while detecting the border
+ img_thresh = cv2.adaptiveThreshold(img_blur, 128, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, thresh_radius, 5)
+ # _, img_thresh = cv2.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC)
+
+ # Dilate the image to emphasize thick borders around the card
+ kernel_dilate = np.ones((dilate_radius, dilate_radius), np.uint8)
+ img_dilate = cv2.dilate(img_thresh, kernel_dilate, iterations=1)
+ img_dilate = cv2.erode(img_dilate, kernel_dilate, iterations=1)
+
+ img_contour = img_dilate.copy()
+ _, contours, _ = cv2.findContours(img_contour, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+ img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
+ img_contour = cv2.drawContours(img_contour, contours, -1, (128, 0, 0), 1)
+
+ # find the biggest area
+ c = max(contours, key=cv2.contourArea)
+
+ x, y, w, h = cv2.boundingRect(c)
+ # draw the book contour (in green)
+ img_contour = cv2.drawContours(img_contour, [c], -1, (0, 255, 0), 1)
+
+ # Canny edge - low minimum hysteresis to detect glowed area,
+ # and high maximum hysteresis to compensate for high false positives.
+ img_canny = cv2.Canny(img_dilate, min_hyst, max_hyst)
+
+ detected_lines = cv2.HoughLinesP(img_dilate, 1, np.pi / 180, threshold=300,
+ minLineLength=min_line_length,
+ maxLineGap=max_line_gap)
+ card_found = detected_lines is not None
+ if card_found:
+ print(len(detected_lines))
+
+ img_hough = cv2.cvtColor(img_canny.copy(), cv2.COLOR_GRAY2BGR)
+ if card_found:
+ for line in detected_lines:
+ x1, y1, x2, y2 = line[0]
+ cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 1)
+
+ img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
+ img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR)
+ #img_canny = cv2.cvtColor(img_canny, cv2.COLOR_GRAY2BGR)
+ return img_thresh, img_dilate, img_contour, img_hough
+
+
def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
+ img_copy = img.copy()
# Create a 4D blob from a frame.
blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)
@@ -94,8 +192,19 @@
if out_path is not None:
cv2.imwrite(out_path, img.astype(np.uint8))
if display:
- cv2.imshow('result', img)
+ no_glare = remove_glare(img_copy)
+ img_concat = np.concatenate((img, no_glare), axis=1)
+ cv2.imshow('result', img_concat)
+ for i in range(len(obj_list)):
+ class_id, confidence, box = obj_list[i]
+ left, top, width, height = box
+ img_snip = img_copy[max(0, top):min(img.shape[0], top + height),
+ max(0, left):min(img.shape[1], left + width)]
+ img_thresh, img_dilate, img_canny, img_hough = find_card(img_snip)
+ img_concat = np.concatenate((img_snip, img_thresh, img_dilate, img_canny, img_hough), axis=1)
+ cv2.imshow('feature#%d' % i, img_concat)
cv2.waitKey(0)
+ cv2.destroyAllWindows()
return obj_list
@@ -105,6 +214,7 @@
vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
(round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
+ max_num_obj = 0
while True:
ret, frame = capture.read()
if not ret:
@@ -112,10 +222,26 @@
print("End of video. Press any key to exit")
cv2.waitKey(0)
break
+ img = frame.copy()
obj_list = detect_frame(net, classes, frame, thresh_conf=thresh_conf, thresh_nms=thresh_nms, in_dim=in_dim,
display=False, out_path=None)
+ max_num_obj = max(max_num_obj, len(obj_list))
if display:
- cv2.imshow('result', frame)
+ no_glare = remove_glare(img)
+ img_concat = np.concatenate((frame, no_glare), axis=1)
+ cv2.imshow('result', img_concat)
+ for i in range(len(obj_list)):
+ class_id, confidence, box = obj_list[i]
+ left, top, width, height = box
+ img_snip = img[max(0, top):min(img.shape[0], top + height),
+ max(0, left):min(img.shape[1], left + width)]
+ img_thresh, img_dilate, img_canny, img_hough = find_card(img_snip)
+ img_concat = np.concatenate((img_snip, img_thresh, img_dilate, img_canny, img_hough), axis=1)
+ cv2.imshow('feature#%d' % i, img_concat)
+ for i in range(len(obj_list), max_num_obj):
+ cv2.imshow('feature#%d' % i, np.zeros((1, 1), dtype=np.uint8))
+ if len(obj_list) > 0:
+ cv2.waitKey(0)
if out_path is not None:
vid_writer.write(frame.astype(np.uint8))
cv2.waitKey(1)
@@ -127,7 +253,7 @@
def main():
# Specify paths for all necessary files
- test_path = os.path.abspath('../data/test1.mp4')
+ test_path = os.path.abspath('../data/test1.jpg')
weight_path = 'weights/second_general/tiny_yolo_final.weights'
cfg_path = 'cfg/tiny_yolo.cfg'
class_path = "data/obj.names"
--
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