import cv2
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import numpy as np
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import imagehash as ih
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import os
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import sys
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import math
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import random
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from operator import itemgetter
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card_width = 315
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card_height = 440
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# Disclaimer: majority of the basic framework in this file is modified from the following tutorial:
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# https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
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# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
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def order_points(pts):
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# initialzie a list of coordinates that will be ordered
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# such that the first entry in the list is the top-left,
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# the second entry is the top-right, the third is the
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# bottom-right, and the fourth is the bottom-left
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rect = np.zeros((4, 2), dtype="float32")
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# the top-left point will have the smallest sum, whereas
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# the bottom-right point will have the largest sum
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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# now, compute the difference between the points, the
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# top-right point will have the smallest difference,
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# whereas the bottom-left will have the largest difference
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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# return the ordered coordinates
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return rect
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def four_point_transform(image, pts):
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# obtain a consistent order of the points and unpack them
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# individually
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rect = order_points(pts)
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(tl, tr, br, bl) = rect
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# compute the width of the new image, which will be the
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# maximum distance between bottom-right and bottom-left
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# x-coordiates or the top-right and top-left x-coordinates
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widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
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widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
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maxWidth = max(int(widthA), int(widthB))
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# compute the height of the new image, which will be the
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# maximum distance between the top-right and bottom-right
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# y-coordinates or the top-left and bottom-left y-coordinates
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heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
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heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
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maxHeight = max(int(heightA), int(heightB))
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# now that we have the dimensions of the new image, construct
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# the set of destination points to obtain a "birds eye view",
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# (i.e. top-down view) of the image, again specifying points
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# in the top-left, top-right, bottom-right, and bottom-left
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# order
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dst = np.array([
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[0, 0],
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[maxWidth - 1, 0],
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[maxWidth - 1, maxHeight - 1],
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[0, maxHeight - 1]], dtype="float32")
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# compute the perspective transform matrix and then apply it
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M = cv2.getPerspectiveTransform(rect, dst)
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warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
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# If the image is horizontally long, rotate it by 90
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if maxWidth > maxHeight:
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center = (maxHeight / 2, maxHeight / 2)
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M_rot = cv2.getRotationMatrix2D(center, 270, 1.0)
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warped = cv2.warpAffine(warped, M_rot, (maxHeight, maxWidth))
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# return the warped image
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return warped
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# Get the names of the output layers
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def get_outputs_names(net):
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# Get the names of all the layers in the network
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layers_names = net.getLayerNames()
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# Get the names of the output layers, i.e. the layers with unconnected outputs
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return [layers_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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# Remove the bounding boxes with low confidence using non-maxima suppression
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def post_process(frame, outs, thresh_conf, thresh_nms):
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frame_height = frame.shape[0]
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frame_width = frame.shape[1]
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# Scan through all the bounding boxes output from the network and keep only the
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# ones with high confidence scores. Assign the box's class label as the class with the highest score.
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class_ids = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > thresh_conf:
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center_x = int(detection[0] * frame_width)
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center_y = int(detection[1] * frame_height)
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width = int(detection[2] * frame_width)
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height = int(detection[3] * frame_height)
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left = int(center_x - width / 2)
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top = int(center_y - height / 2)
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class_ids.append(class_id)
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confidences.append(float(confidence))
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boxes.append([left, top, width, height])
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# Perform non maximum suppression to eliminate redundant overlapping boxes with lower confidences.
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indices = [ind[0] for ind in cv2.dnn.NMSBoxes(boxes, confidences, thresh_conf, thresh_nms)]
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ret = [[class_ids[i], confidences[i], boxes[i]] for i in indices]
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return ret
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# Draw the predicted bounding box
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def draw_pred(frame, class_id, classes, conf, left, top, right, bottom):
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# Draw a bounding box.
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cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255))
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label = '%.2f' % conf
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# Get the label for the class name and its confidence
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if classes:
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assert (class_id < len(classes))
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label = '%s:%s' % (classes[class_id], label)
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# Display the label at the top of the bounding box
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label_size, base_line = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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top = max(top, label_size[1])
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cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
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def remove_glare(img):
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"""
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Inspired from:
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http://www.amphident.de/en/blog/preprocessing-for-automatic-pattern-identification-in-wildlife-removing-glare.html
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The idea is to find area that has low saturation but high value, which is what a glare usually look like.
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"""
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img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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_, s, v = cv2.split(img_hsv)
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non_sat = (s < 32) * 255 # Find all pixels that are not very saturated
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# Slightly decrease the area of the non-satuared pixels by a erosion operation.
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disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
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non_sat = cv2.erode(non_sat.astype(np.uint8), disk)
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# Set all brightness values, where the pixels are still saturated to 0.
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v[non_sat == 0] = 0
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# filter out very bright pixels.
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glare = (v > 200) * 255
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# Slightly increase the area for each pixel
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glare = cv2.dilate(glare.astype(np.uint8), disk)
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glare_reduced = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 200
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glare = cv2.cvtColor(glare, cv2.COLOR_GRAY2BGR)
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corrected = np.where(glare, glare_reduced, img)
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return corrected
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def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.3):
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# Typical pre-processing - grayscale, blurring, thresholding
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_blur = cv2.medianBlur(img_gray, 5)
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img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 5, thresh_c)
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# Dilute the image, then erode them to remove minor noises
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kernel = np.ones(kernel_size, np.uint8)
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img_dilate = cv2.dilate(img_thresh, kernel, iterations=1)
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img_erode = cv2.erode(img_dilate, kernel, iterations=1)
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# Find the contour
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#img_contour = img_erode.copy()
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_, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(cnts) == 0:
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print('no contours')
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return []
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#img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
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# For each contours detected, check if they are large enough and are rectangle
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cnts_rect = []
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ind_sort = sorted(range(len(cnts)), key=lambda i: cv2.contourArea(cnts[i]), reverse=True)
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for i in range(len(cnts)):
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size = cv2.contourArea(cnts[ind_sort[i]])
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peri = cv2.arcLength(cnts[ind_sort[i]], True)
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approx = cv2.approxPolyDP(cnts[ind_sort[i]], 0.04 * peri, True)
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if size > img.shape[0] * img.shape[1] * size_ratio and len(approx) == 4:
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cnts_rect.append(approx)
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return cnts_rect
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'''
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#card_dim = [630, 880]
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#for cnt in cnts_rect:
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# pts = np.float32([p[0] for p in cnt])
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# img_warp = four_point_transform(img, pts)
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# Check which side is longer
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len_1 = math.sqrt((cnt[0][0][0] - cnt[1][0][0]) ** 2 + (cnt[0][0][1] - cnt[1][0][1]) ** 2)
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len_2 = math.sqrt((cnt[0][0][0] - cnt[-1][0][0]) ** 2 + (cnt[0][0][1] - cnt[-1][0][1]) ** 2)
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#print(len_1, len_2)
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orig_corner = np.array([p[0] for p in cnt], dtype=np.float32)
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if len_1 > len_2:
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new_corner = np.array([[0, 0], [0, card_dim[1]], [card_dim[0], card_dim[1]], [card_dim[0], 0]], dtype=np.float32)
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else:
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new_corner = np.array([[0, 0], [card_dim[0], 0], [card_dim[0], card_dim[1]], [0, card_dim[1]]],
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dtype=np.float32)
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M = cv2.getPerspectiveTransform(orig_corner, new_corner)
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img_warp = cv2.warpPerspective(img, M, (card_dim[0], card_dim[1]))
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#cv2.imshow('warp', img_warp)
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#cv2.waitKey(0)
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#img_contour = cv2.drawContours(img_contour, cnts_rect, -1, (0, 255, 0), 3)
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#img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
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#img_erode = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR)
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#img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR)
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#return img_thresh, img_erode, img_contour
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'''
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def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
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img_copy = img.copy()
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# Create a 4D blob from a frame.
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blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)
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# Sets the input to the network
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net.setInput(blob)
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# Runs the forward pass to get output of the output layers
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outs = net.forward(get_outputs_names(net))
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# Remove the bounding boxes with low confidence
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obj_list = post_process(img, outs, thresh_conf, thresh_nms)
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for obj in obj_list:
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class_id, confidence, box = obj
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left, top, width, height = box
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draw_pred(img, class_id, classes, confidence, left, top, left + width, top + height)
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# Put efficiency information. The function getPerfProfile returns the
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# overall time for inference(t) and the timings for each of the layers(in layersTimes)
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t, _ = net.getPerfProfile()
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label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
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cv2.putText(img, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
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if out_path is not None:
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cv2.imwrite(out_path, img.astype(np.uint8))
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if display:
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#no_glare = remove_glare(img_copy)
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#img_concat = np.concatenate((img, no_glare), axis=1)
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cv2.imshow('result', img)
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'''
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for i in range(len(obj_list)):
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class_id, confidence, box = obj_list[i]
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left, top, width, height = box
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img_snip = img_copy[max(0, top):min(img.shape[0], top + height),
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max(0, left):min(img.shape[1], left + width)]
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img_thresh, img_dilate, img_canny, img_hough = find_card(img_snip)
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img_concat = np.concatenate((img_snip, img_thresh, img_dilate, img_canny, img_hough), axis=1)
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cv2.imshow('feature#%d' % i, img_concat)
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'''
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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return obj_list
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def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
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if out_path is not None:
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vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
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(round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
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round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
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max_num_obj = 0
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while True:
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ret, frame = capture.read()
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if not ret:
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# End of video
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print("End of video. Press any key to exit")
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cv2.waitKey(0)
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break
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img = frame.copy()
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obj_list = detect_frame(net, classes, frame, thresh_conf=thresh_conf, thresh_nms=thresh_nms, in_dim=in_dim,
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display=False, out_path=None)
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#cnts_rect = find_card(img)
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max_num_obj = max(max_num_obj, len(obj_list))
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if display:
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img_result = frame.copy()
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#img_result = cv2.drawContours(img_result, cnts_rect, -1, (0, 255, 0), 2)
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#for i in range(len(cnts_rect)):
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# pts = np.float32([p[0] for p in cnts_rect[i]])
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# img_warp = four_point_transform(img, pts)
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# cv2.imshow('card#%d' % i, img_warp)
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#for i in range(len(cnts_rect), max_num_obj):
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# cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
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#no_glare = remove_glare(img)
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#img_thresh, img_erode, img_contour = find_card(no_glare)
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#img_concat = np.concatenate((no_glare, img_contour), axis=1)
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for i in range(len(obj_list)):
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class_id, confidence, box = obj_list[i]
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left, top, width, height = box
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offset_ratio = 0.1
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x1 = max(0, int(left - offset_ratio * width))
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x2 = min(img.shape[1], int(left + (1 + offset_ratio) * width))
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y1 = max(0, int(top - offset_ratio * height))
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y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height))
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img_snip = img[y1:y2, x1:x2]
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cnts = find_card(img_snip)
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if len(cnts) > 0:
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cnt = cnts[-1]
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pts = np.float32([p[0] for p in cnt])
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img_warp = four_point_transform(img_snip, pts)
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img_warp = cv2.resize(img_warp, (card_width, card_height))
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#img_thresh, img_dilate, img_contour = find_card(img_snip)
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#img_concat = np.concatenate((img_snip, img_contour), axis=1)
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cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2)
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cv2.imshow('card#%d' % i, img_warp)
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else:
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cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
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for i in range(len(obj_list), max_num_obj):
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cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
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cv2.imshow('result', img_result)
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#if len(obj_list) > 0:
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# cv2.waitKey(0)
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if out_path is not None:
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vid_writer.write(frame.astype(np.uint8))
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cv2.waitKey(1)
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if out_path is not None:
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vid_writer.release()
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cv2.destroyAllWindows()
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def main():
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# Specify paths for all necessary files
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test_path = os.path.abspath('../data/test4.mp4')
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#weight_path = 'backup/tiny_yolo_10_39500.weights'
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#cfg_path = 'cfg/tiny_yolo_10.cfg'
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#class_path = "data/obj_10.names"
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weight_path = 'weights/second_general/tiny_yolo_final.weights'
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cfg_path = 'cfg/tiny_yolo.cfg'
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class_path = 'data/obj.names'
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out_dir = 'out'
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if not os.path.isfile(test_path):
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print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
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return
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if not os.path.isfile(weight_path):
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print('The weight file %s doesn\'t exist!' % os.path.abspath(test_path))
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return
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if not os.path.isfile(cfg_path):
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print('The config file %s doesn\'t exist!' % os.path.abspath(test_path))
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return
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if not os.path.isfile(class_path):
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print('The class file %s doesn\'t exist!' % os.path.abspath(test_path))
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return
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thresh_conf = 0.01
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thresh_nms = 0.8
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# Setup
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# Read class names from text file
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with open(class_path, 'r') as f:
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classes = [line.strip() for line in f.readlines()]
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# Load up the neural net using the config and weights
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net = cv2.dnn.readNetFromDarknet(cfg_path, weight_path)
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net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
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net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
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# Save the detection result if out_dir is provided
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if out_dir is None or out_dir == '':
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out_path = None
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else:
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out_path = out_dir + '/' + os.path.split(test_path)[1]
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# Check if test file is image or video
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test_ext = test_path[test_path.find('.') + 1:]
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if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']:
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img = cv2.imread(test_path)
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detect_frame(net, classes, img, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms)
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else:
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capture = cv2.VideoCapture(0)
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detect_video(net, classes, capture, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms)
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capture.release()
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pass
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if __name__ == '__main__':
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main()
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