renamed app2

app.py

@@ -1,9 +1,165 @@
-import cv2 as cv
-import numpy as n
+import numpy as np
+from ultralytics import YOLO
+import cv2
+import cvzone
+import math
+from sort import *
 
-cap=cv.VideoCapture(0)
+
+
+# MongoDB connection URI with a default database (replace with your actual values)
+
+# def connectMongo() -> pymongo.database.Database:
+#     try:
+      
+
+#         try:
+            
+#             client = pymongo.MongoClient('mongodb+srv://INFINIX:INFINIX@cluster0.rubyoda.mongodb.net/?retryWrites=true&w=majority')
+#             db = client["INFINIX"]
+#             print(db)
+#             if db != None:
+#                 print("connected to db")
+#                 return db 
+#         except Exception as e:
+#             print(e)
+
+    
+#     except Exception as e:
+#         return "Error in Connecting to MongoDB" + str(e)
+    
+# db=connectMongo()
+
+# collection=db['BUS_DETS']
+
+# Create a collection to store entry count
+# entry_count_collection = db.entry_count
+
+cap = cv2.VideoCapture('TrialFootage.mp4')
+
+model = YOLO("../Yolo-Weights/yolov8n.pt")
+occupancy = 0
+coming = 0
+goin = 0
+ListPeople = []
+dict = {}
+width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+print(f"Video Resolution: {width}x{height}")
+classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
+              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
+              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
+              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
+              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
+              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
+              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
+              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
+              "teddy bear", "hair drier", "toothbrush"
+              ]
+
+# Tracking
+
+tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3)
+
+yelloLine = [270, 0, 270, 600]
+
+RedLine = [173, 0, 173, 600]
+
+totalCountUp = []
+mask=cv2.imread('mask.jpg')
 
 while True:
-    success,img=cap.read()
-    cv.imshow("Video",img)
-cv2.waitKey(1)
+    
+
+    success, img = cap.read()
+    # imgRegion=cv2.bitwise_and(img,mask)
+
+    results = model(img, stream=True)
+
+    detections = np.empty((0, 5))
+
+    for r in results:
+        boxes = r.boxes
+        for box in boxes:
+            # Bounding Box
+            x1, y1, x2, y2 = box.xyxy[0]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            # cv2.rectangle(img,(x1,y1),(x2,y2),(255,0,255),3)
+            w, h = x2 - x1, y2 - y1
+
+            # Confidence
+            conf = math.ceil((box.conf[0] * 100)) / 100
+            # Class Name
+            cls = int(box.cls[0])
+            currentClass = classNames[cls]
+
+            if currentClass == "person" and conf > 0.3:
+                # cvzone.putTextRect(img, f'{currentClass} {conf}', (max(0, x1), max(35, y1)),
+                #                    scale=0.6, thickness=1, offset=3)
+                # cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=5)
+                currentArray = np.array([x1, y1, x2, y2, conf])
+                detections = np.vstack((detections, currentArray))
+
+    resultsTracker = tracker.update(detections)
+
+    
+    for result in resultsTracker:
+        x1, y1, x2, y2, id = result
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        print(result)
+        w, h = x2 - x1, y2 - y1
+        cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=2, colorR=(255, 0, 255))
+        cvzone.putTextRect(img, f' {int(id)}', (max(0, x1), max(35, y1)),
+                           scale=2, thickness=3, offset=10)
+
+        cx, cy = x1 + w // 2, y1 + h // 2
+        cv2.circle(img, (cx, cy), 5, (255, 0, 255), cv2.FILLED)
+
+        if yelloLine[0] - 20 < cx < yelloLine[2] + 20:
+            if totalCountUp.count(id) == 0:
+                totalCountUp.append(id)
+                dict[id] = [False]
+                cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5)
+            elif totalCountUp.count(id) == 1:
+                if (dict[id].count(False) < 1):
+                    dict[id].append(False)
+                cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5)
+        if RedLine[0] - 20 < cx < RedLine[2] + 30:
+            if totalCountUp.count(id) == 0:
+                totalCountUp.append(id)
+                dict[id] = [True]
+                cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5)
+            elif totalCountUp.count(id) == 1:
+                if (dict[id].count(True) < 1):
+                    dict[id].append(True)
+                    cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5)
+
+    print(totalCountUp)
+    entry_count = 0
+
+    for i in dict.values():
+        if (len(i) == 2):
+            if i[0] == True and i[1] == False:
+                if entry_count > 0:
+                    entry_count -= 1
+            if i[0] == False and i[1] == True:
+                entry_count += 1
+        
+    
+    print('count is ', entry_count)
+    print(dict)
+    # # Update the MongoDB collection with the current count
+    # entry_count_collection.update_one({}, {"$set": {"count":entry_count}}, upsert=True)
+    # collection.update_one({"id": "your_document_id"}, {"$set": {"entry_count": entry_count}})
+    
+    cv2.putText(img, str(entry_count), (110, 245), cv2.FONT_HERSHEY_PLAIN, 5, (50, 50, 230), 7)
+    print('count is ', entry_count)
+    print(dict)
+    cv2.imshow("Image", img)
+    cv2.waitKey(1)
+    print(entry_count)
+    # collection.update_one({"id":"826587"},
+    #                {"$set": {
+    #                    "entry_count":entry_count,
+    #                   }})

cam.py

@@ -0,0 +1,9 @@
+import cv2 as cv
+import numpy as n
+
+cap=cv.VideoCapture(0)
+
+while True:
+    success,img=cap.read()
+    cv.imshow("Video",img)
+cv2.waitKey(1)

dummy.py

@@ -1,165 +0,0 @@
-import numpy as np
-from ultralytics import YOLO
-import cv2
-import cvzone
-import math
-from sort import *
-
-
-
-# MongoDB connection URI with a default database (replace with your actual values)
-
-# def connectMongo() -> pymongo.database.Database:
-#     try:
-      
-
-#         try:
-            
-#             client = pymongo.MongoClient('mongodb+srv://INFINIX:INFINIX@cluster0.rubyoda.mongodb.net/?retryWrites=true&w=majority')
-#             db = client["INFINIX"]
-#             print(db)
-#             if db != None:
-#                 print("connected to db")
-#                 return db 
-#         except Exception as e:
-#             print(e)
-
-    
-#     except Exception as e:
-#         return "Error in Connecting to MongoDB" + str(e)
-    
-# db=connectMongo()
-
-# collection=db['BUS_DETS']
-
-# Create a collection to store entry count
-# entry_count_collection = db.entry_count
-
-cap = cv2.VideoCapture('TrialFootage.mp4')
-
-model = YOLO("../Yolo-Weights/yolov8n.pt")
-occupancy = 0
-coming = 0
-goin = 0
-ListPeople = []
-dict = {}
-width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-print(f"Video Resolution: {width}x{height}")
-classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
-              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
-              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
-              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
-              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
-              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
-              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
-              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
-              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
-              "teddy bear", "hair drier", "toothbrush"
-              ]
-
-# Tracking
-
-tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3)
-
-yelloLine = [270, 0, 270, 600]
-
-RedLine = [173, 0, 173, 600]
-
-totalCountUp = []
-mask=cv2.imread('mask.jpg')
-
-while True:
-    
-
-    success, img = cap.read()
-    # imgRegion=cv2.bitwise_and(img,mask)
-
-    results = model(img, stream=True)
-
-    detections = np.empty((0, 5))
-
-    for r in results:
-        boxes = r.boxes
-        for box in boxes:
-            # Bounding Box
-            x1, y1, x2, y2 = box.xyxy[0]
-            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-            # cv2.rectangle(img,(x1,y1),(x2,y2),(255,0,255),3)
-            w, h = x2 - x1, y2 - y1
-
-            # Confidence
-            conf = math.ceil((box.conf[0] * 100)) / 100
-            # Class Name
-            cls = int(box.cls[0])
-            currentClass = classNames[cls]
-
-            if currentClass == "person" and conf > 0.3:
-                # cvzone.putTextRect(img, f'{currentClass} {conf}', (max(0, x1), max(35, y1)),
-                #                    scale=0.6, thickness=1, offset=3)
-                # cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=5)
-                currentArray = np.array([x1, y1, x2, y2, conf])
-                detections = np.vstack((detections, currentArray))
-
-    resultsTracker = tracker.update(detections)
-
-    
-    for result in resultsTracker:
-        x1, y1, x2, y2, id = result
-        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-        print(result)
-        w, h = x2 - x1, y2 - y1
-        cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=2, colorR=(255, 0, 255))
-        cvzone.putTextRect(img, f' {int(id)}', (max(0, x1), max(35, y1)),
-                           scale=2, thickness=3, offset=10)
-
-        cx, cy = x1 + w // 2, y1 + h // 2
-        cv2.circle(img, (cx, cy), 5, (255, 0, 255), cv2.FILLED)
-
-        if yelloLine[0] - 20 < cx < yelloLine[2] + 20:
-            if totalCountUp.count(id) == 0:
-                totalCountUp.append(id)
-                dict[id] = [False]
-                cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5)
-            elif totalCountUp.count(id) == 1:
-                if (dict[id].count(False) < 1):
-                    dict[id].append(False)
-                cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5)
-        if RedLine[0] - 20 < cx < RedLine[2] + 30:
-            if totalCountUp.count(id) == 0:
-                totalCountUp.append(id)
-                dict[id] = [True]
-                cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5)
-            elif totalCountUp.count(id) == 1:
-                if (dict[id].count(True) < 1):
-                    dict[id].append(True)
-                    cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5)
-
-    print(totalCountUp)
-    entry_count = 0
-
-    for i in dict.values():
-        if (len(i) == 2):
-            if i[0] == True and i[1] == False:
-                if entry_count > 0:
-                    entry_count -= 1
-            if i[0] == False and i[1] == True:
-                entry_count += 1
-        
-    
-    print('count is ', entry_count)
-    print(dict)
-    # # Update the MongoDB collection with the current count
-    # entry_count_collection.update_one({}, {"$set": {"count":entry_count}}, upsert=True)
-    # collection.update_one({"id": "your_document_id"}, {"$set": {"entry_count": entry_count}})
-    
-    cv2.putText(img, str(entry_count), (110, 245), cv2.FONT_HERSHEY_PLAIN, 5, (50, 50, 230), 7)
-    print('count is ', entry_count)
-    print(dict)
-    cv2.imshow("Image", img)
-    cv2.waitKey(1)
-    print(entry_count)
-    # collection.update_one({"id":"826587"},
-    #                {"$set": {
-    #                    "entry_count":entry_count,
-    #                   }})