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main.py
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main.py
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import cv2
import numpy as np
import glob
import random
import easyocr
reader = easyocr.Reader(['en'])
# Load Yolo
net = cv2.dnn.readNet("yolov3last2.weights", "yolov3.cfg")
# Name custom object
classes = ["name",'dob','gender','aadhar_no']
# Images path
images_path = glob.glob('1.jpg')
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# Insert here the path of your images
random.shuffle(images_path)
# loop through all the images
for img_path in images_path:
# Loading image
img = cv2.imread(img_path)
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape
# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.25:
# Object detected
# print(class_id)
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# print(indexes)
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
x=x-2
y=y-4
h=h+5
w=w+5
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
# cv2.putText(img, label, (x, y + 30), font, 3, color, 2)
crop = img[y:y+h, x:x+w]
# cv2.imshow("temp", crop)
# key = cv2.waitKey(0)
result = reader.readtext(crop,detail=0)
print(result)
cv2.rectangle(img, (x, y), (x + w, y + h), color, 1)
cv2.imshow("Image", img)
key = cv2.waitKey(0)
cv2.destroyAllWindows()