project

Author: JpThomaz

alpr_app/.ipynb_checkpoints/OCR-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e35d918a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from paddleocr import PaddleOCR, draw_ocr # main OCR dependencies\n",
+    "from matplotlib import pyplot as plt # plot images\n",
+    "import cv2 #opencv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "93004937",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2022/05/23 23:43:08] ppocr DEBUG: Namespace(help='==SUPPRESS==', use_gpu=False, ir_optim=True, use_tensorrt=False, min_subgraph_size=15, precision='fp32', gpu_mem=500, image_dir=None, det_algorithm='DB', det_model_dir='C:\\\\Users\\\\jpedr/.paddleocr/whl\\\\det\\\\en\\\\en_ppocr_mobile_v2.0_det_infer', det_limit_side_len=960, det_limit_type='max', det_db_thresh=0.3, det_db_box_thresh=0.6, det_db_unclip_ratio=1.5, max_batch_size=10, use_dilation=False, det_db_score_mode='fast', det_east_score_thresh=0.8, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_sast_score_thresh=0.5, det_sast_nms_thresh=0.2, det_sast_polygon=False, det_pse_thresh=0, det_pse_box_thresh=0.85, det_pse_min_area=16, det_pse_box_type='quad', det_pse_scale=1, scales=[8, 16, 32], alpha=1.0, beta=1.0, fourier_degree=5, det_fce_box_type='poly', rec_algorithm='CRNN', rec_model_dir='C:\\\\Users\\\\jpedr/.paddleocr/whl\\\\rec\\\\en\\\\en_number_mobile_v2.0_rec_infer', rec_image_shape='3, 32, 320', rec_batch_num=6, max_text_length=25, rec_char_dict_path='C:\\\\Users\\\\jpedr\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python39\\\\lib\\\\site-packages\\\\paddleocr\\\\ppocr\\\\utils\\\\en_dict.txt', use_space_char=True, vis_font_path='./doc/fonts/simfang.ttf', drop_score=0.5, e2e_algorithm='PGNet', e2e_model_dir=None, e2e_limit_side_len=768, e2e_limit_type='max', e2e_pgnet_score_thresh=0.5, e2e_char_dict_path='./ppocr/utils/ic15_dict.txt', e2e_pgnet_valid_set='totaltext', e2e_pgnet_mode='fast', use_angle_cls=False, cls_model_dir='C:\\\\Users\\\\jpedr/.paddleocr/whl\\\\cls\\\\ch_ppocr_mobile_v2.0_cls_infer', cls_image_shape='3, 48, 192', label_list=['0', '180'], cls_batch_num=6, cls_thresh=0.9, enable_mkldnn=False, cpu_threads=10, use_pdserving=False, warmup=False, draw_img_save_dir='./inference_results', save_crop_res=False, crop_res_save_dir='./output', use_mp=False, total_process_num=1, process_id=0, benchmark=False, save_log_path='./log_output/', show_log=True, use_onnx=False, output='./output', table_max_len=488, table_model_dir=None, table_char_type='en', table_char_dict_path=None, layout_path_model='lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config', layout_label_map=None, mode='structure', lang='en', det=True, rec=True, type='ocr', ocr_version='PP-OCRv2', structure_version='STRUCTURE')\n"
+     ]
+    }
+   ],
+   "source": [
+    "ocr_model = PaddleOCR(lang='en')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "16d43ff3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2022/05/23 23:43:09] ppocr WARNING: Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process\n",
+      "[2022/05/23 23:43:09] ppocr DEBUG: dt_boxes num : 1, elapse : 0.03198885917663574\n",
+      "[2022/05/23 23:43:09] ppocr DEBUG: rec_res num  : 1, elapse : 0.05398249626159668\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = ocr_model.ocr('cropped.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8e2ce288",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[[[12.0, 11.0], [127.0, 26.0], [122.0, 52.0], [6.0, 37.0]],\n",
+       "  ('IZC840', 0.84395915)]]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "31e1ded4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IZC840\n"
+     ]
+    }
+   ],
+   "source": [
+    "for res in result:\n",
+    "    print(res[1][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b2d187",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

alpr_app/.ipynb_checkpoints/alpr-checkpoint.ipynb

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+
+from IPython.display import Image
+from matplotlib import pyplot as plt
+from PIL import Image, ImageFont, ImageDraw
+import cv2
+import numpy as np
+import pandas as pd
+import requests
+import time
+
+#input_path = r'test_dataset/okk7448.jpg'
+
+#Image(input_path)
+cap = cv2.VideoCapture(0)
+confThreshold = 0.5  
+nmsThreshold = 0.4
+inpWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))     
+inpHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))     
+
+classesFile = "C:/Users/jpedr/TCC/ALPR_final/alpr_app/yolo_utils/classes.names";
+classes = None
+with open(classesFile, 'rt') as f:
+    classes = f.read().rstrip('\n').split('\n')
+
+modelConfiguration = r"C:/Users/jpedr/TCC/ALPR_final/alpr_app/yolo_utils/darknet-yolov3.cfg";
+modelWeights = r"C:/Users/jpedr/TCC/ALPR_final/alpr_app/yolo_utils/lapi.weights";
+
+net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
+net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
+net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
+
+
+def getOutputsNames(net):
+    # Get the names of all the layers in the network
+    layersNames = net.getLayerNames()
+    # Get the names of the output layers, i.e. the layers with unconnected outputs
+    return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+
+def drawPred(classId, conf, left, top, right, bottom, frame):
+    cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3)
+    label = '%.2f' % conf
+    if classes:
+        assert(classId < len(classes))
+        label = '%s:%s' % (classes[classId], label)
+    labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+    top = max(top, labelSize[1])
+    cv2.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (0, 0, 255), cv2.FILLED)
+    #cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine),    (255, 255, 255), cv.FILLED)
+    cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 2)
+
+def postprocess(frame, outs):
+    frameHeight = frame.shape[0]
+    frameWidth = frame.shape[1]
+    
+
+    classIds = []
+    confidences = []
+    boxes = []
+    for out in outs:
+       
+        for detection in out:
+
+            scores = detection[5:]
+            classId = np.argmax(scores)
+          
+            confidence = scores[classId]
+            '''if detection[4]>confThreshold:
+                print(detection[4], " - ", scores[classId], " - th : ", confThreshold)
+                print(detection)'''
+            if confidence > confThreshold:
+                center_x = int(detection[0] * frameWidth)
+                center_y = int(detection[1] * frameHeight)
+                width = int(detection[2] * frameWidth)
+                height = int(detection[3] * frameHeight)
+                left = int(center_x - width / 2)
+                top = int(center_y - height / 2)
+                classIds.append(classId)
+                confidences.append(float(confidence))
+                boxes.append([left, top, width, height])
+
+    
+    cropped=None
+    indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
+    for i in indices:
+        i = i[0]
+        box = boxes[i]
+        left = box[0]
+        top = box[1]
+        width = box[2]
+        height = box[3]
+        
+
+        bottom = top + height
+        right = left + width
+        
+  
+        cropped = frame[top:bottom, left:right].copy()
+
+        drawPred(classIds[i], confidences[i], left, top, right, bottom, frame)
+    if cropped is not None:
+        return cropped
+
+def put_text(image,position,text,size=16,color_text=(239, 255, 233)):
+    im_pil = Image.fromarray(image)
+    font = ImageFont.truetype("C:/Users/jpedr/TCC/ALPR_final/alpr_app/font.ttf", size=size)
+    draw = ImageDraw.Draw(im_pil)
+    draw.text(position, text, fill=color_text, font=font)
+    image = np.asarray(im_pil)
+    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image
+
+# RECONHECIMENTO DE PLACA
+class camera:
+
+    crawl= None
+    captura = None
+    print('iniciando camera')
+    while cv2.waitKey(1) < 0:
+
+        hasFrame, frame = cap.read() #frame: an image object from cv2
+        blob = cv2.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop=False)
+        net.setInput(blob)
+        outs = net.forward(getOutputsNames(net))
+        cropped = postprocess(frame, outs)
+        cv2.rectangle(frame,(0,0),(inpWidth,70),(32, 32, 32),-1)
+        if cropped is not None:
+            cv2.imwrite('C:/Users/jpedr/TCC/ALPR_final/alpr_app/cropped.jpg',cropped)
+            url = 'http://127.0.0.1:5000/ocr'
+            x = requests.post(url)
+            placa=x.json()
+            print(placa)
+            frame = put_text(frame, (inpWidth -100,inpHeight -30),placa)
+            if  len(placa) == 7 and placa != crawl:
+                try:
+                    all_infos = []
+                    url = 'http://127.0.0.1:5000/crawler'
+                    myobj = {'placa': placa}
+                    x = requests.post(url, json = myobj)
+                    info = x.json()
+                    #print(info)A
+                    if info:
+                        all_infos.append(info)
+                        plate_resource = info[-1]
+                        venal = info[0].replace(".",",")
+                        if venal == "Aliquota:": venal = "Não Estimado"
+                        else: venal = "R$ " + info[0].replace(".",",")
+                        marca = info[1] + ' '+info[2]+'/'+''+info[5]
+                        cidade = info[10]+'/'+info[9]
+                        headers = {"User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}
+                        req = requests.get(plate_resource,headers=headers).content
+                        plate = 'C:/Users/jpedr/TCC/ALPR_final/alpr_app/plate.webp'
+                        with open(plate, 'wb+') as f: f.write(req)
+                        frame = put_text(frame, (175,5),'Modelo: '+marca)
+                        frame = put_text(frame, (175,35),'Valor: '+venal)
+                        frame = put_text(frame, (375,35),'Local: '+cidade)
+                        try:
+                            plate = cv2.imread(plate)   
+                            plate = cv2.resize(plate,(150,50))  
+                            frame[10:10+50, 5:5+150] = plate
+                        except:
+                            pass 
+                        crawl = placa
+                        captura=[marca,venal,cidade]
+                except Exception as e:
+                    print(str(e))
+
+        #roi[np.where(mask)] = 0
+        if captura != None:
+            plate = 'C:/Users/jpedr/TCC/ALPR_final/alpr_app/plate.webp'
+            frame = put_text(frame, (175,5),'Modelo: '+captura[0])
+            frame = put_text(frame, (175,35),'Valor: '+captura[1])
+            frame = put_text(frame, (375,35),'Local: '+captura[2])
+            plate = cv2.imread(plate)   
+            plate = cv2.resize(plate,(150,50))   
+            frame[10:10+50, 5:5+150] = plate
+        frames = frame
+        #return frames
+        #cv2.imshow("CAPTURA ALPR",frame)
+        #time.sleep(1)
+
+
+#cam()
+
+
+